MTF+MA V2 MTF+MA Indicator by ridvansozen1
The MTF+MA Indicator is a multi-timeframe moving average strategy developed by TradingView user ridvansozen1. This tool is designed to assist cryptocurrency traders in identifying potential long and short trading opportunities by analyzing market trends across multiple timeframes.
Key Features:
Multi-Timeframe Analysis: Utilizes fast and slow exponential moving averages (EMAs) on user-defined long, mid, and short-term timeframes to assess market direction.
Signal Generation: Generates long signals when all selected timeframes indicate a positive trend and short signals when all indicate a negative trend.
Customizable Parameters: Allows users to adjust source data, EMA lengths, and timeframes to tailor the strategy to specific trading preferences.
Date Range Filtering: Includes settings to define specific date ranges for signal generation, enabling focused backtesting and analysis.
How to Use:
Configure Moving Averages: Set your preferred lengths for the fast and slow EMAs.
Select Timeframes: Choose the long, mid, and short-term timeframes that align with your trading strategy.
Set Date Range: Define the date range during which the strategy should generate signals.
Interpret Signals: Monitor the indicator plots—green, blue, and red lines representing the EMA differences across timeframes. A long position is suggested when all three lines are above zero, and a short position is suggested when all are below zero.
Disclaimer: This indicator is intended for educational purposes and should not be considered financial advice. Users are encouraged to conduct thorough backtesting and apply proper risk management before utilizing this strategy in live trading.
Multitimeframe
Supertrend StrategyThe Supertrend Strategy was created based on the Supertrend and Relative Strength Index (RSI) indicators, widely respected tools in technical analysis. This strategy combines these two indicators to capture market trends with precision and reliability, looking for optimizing exit levels at oversold or overbought price levels.
The Supertrend indicator identifies trend direction based on price and volatility by using the Average True Range (ATR). The ATR measures market volatility by calculating the average range between an asset’s high and low prices over a set period. It provides insight into price fluctuations, with higher ATR values indicating increased volatility and lower values suggesting stability. The Supertrend Indicator plots a line above or below the price, signaling potential buy or sell opportunities: when the price closes above the Supertrend line, an uptrend is indicated, while a close below the line suggests a downtrend. This line shifts as price movements and volatility levels change, acting as both a trailing stop loss and trend confirmation.
To enhance the Supertrend strategy, the Relative Strength Index (RSI) has been added as an exit criterion. As a momentum oscillator, the RSI indicates overbought (usually above 70) or oversold (usually below 30) conditions. This integration allows trades to close when the asset is overbought or oversold, capturing gains before a possible reversal, even if the percentage take profit level has not been reached. This mechanism aims to prevent losses due to market reversals before the Supertrend signal changes.
### Key Features
1. **Entry criteria**:
- The strategy uses the Supertrend indicator calculated by adding or subtracting a multiple of the ATR from the closing price, depending on the trend direction.
- When the price crosses above the Supertrend line, the strategy signals a long (buy) entry. Conversely, when the price crosses below, it signals a short (sell) entry.
- The strategy performs a reversal if there is an open position and a change in the direction of the supertrend occurs
2. **Exit criteria**:
- Take profit of 30% (default) on the average position price.
- Oversold (≤ 5) or overbought (≥ 95) RSI
- Reversal when there is a change in direction of the Supertrend
3. **No Repainting**:
- This strategy is not subject to repainting, as long as the timeframe configured on your chart is the same as the supertrend timeframe .
4. **Position Sizing by Equity and risk management**:
- This strategy has a default configuration to operate with 35% of the equity. At the time of opening the position, the supertrend line is typically positioned at about 12 to 16% of the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity can be adjusted by the user according to their risk management.
5. **Backtest results**:
- This strategy was subjected to deep backtesting and operations in replay mode, including transaction fees of 0.12%, and slippage of 5 ticks.
- The past results in deep backtest and replay mode were compatible and profitable (Variable results depending on the take profit used, supertrend and RSI parameters). However, it should be noted that few operations were evaluated, since the currency in question has been created for a short time and the frequency of operations is relatively small.
- Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
Default Settings
Chart timeframe: 2h
Supertrend Factor: 3.42
ATR period: 14
Supertrend timeframe: 2 h
RSI timeframe: 15 min
RSI Lenght: 5 min
RSI Upper limit: 95
RSI Lower Limit: 5
Take Profit: 30%
BYBIT:1000000MOGUSDT.P
Parent Session Sweeps + Alert Killzone Ranges with Parent Session Sweep
Key Features:
1. Multiple Session Support: The script tracks three major trading sessions - Asia, London, and New York. Users can customize the timing of these sessions.
2. Killzone Visualization: The strategy visually represents each session's range, either as filled boxes or lines, allowing traders to easily identify key price levels.
3. Parent Session Logic: The core of the strategy revolves around identifying a "parent" session - a session that encompasses the range of the following session. This parent session becomes the basis for potential trade setups.
4. Sweep and Reclaim Setups: The strategy looks for price movements that sweep (break above or below) the parent session's high or low, followed by a reclaim of that level. This price action often indicates a potential reversal.
5. Risk-Reward Filtering: Each potential setup is evaluated based on a user-defined minimum risk-reward ratio, ensuring that only high-quality trade opportunities are considered.
6. Candle Close Filter: An optional filter that checks the characteristics of the candle that reclaims the parent session level, adding an extra layer of confirmation to the setup.
7. Performance Tracking: The strategy keeps track of bullish and bearish setup success rates, providing valuable feedback on its performance over time.
8. Visual Aids: The script draws lines to mark the parent session's high and low, making it easy for traders to identify key levels.
How It Works:
1. The script continuously monitors price action across the defined sessions.
2. When a session fully contains the range of the next session, it's identified as a potential parent session.
3. The strategy then waits for price to sweep either the high or low of this parent session.
4. If a sweep occurs, it looks for a reclaim of the swept level within the parameters set by the user.
5. If a valid setup is identified, the script generates an alert and places a trade (if backtesting or running live).
6. The strategy continues to monitor the trade for either reaching the target (opposite level of the parent session) or hitting the stop loss.
Considerations for Signals:
- Sweep: A break of the parent session's high or low.
- Reclaim: A close back inside the parent session range after a sweep.
- Candle Characteristics: Optional filter for the reclaim candle (e.g., bullish candle for long setups).
- Risk-Reward: Each setup must meet or exceed the user-defined minimum risk-reward ratio.
- Session Timing: The strategy is sensitive to the defined session times, which should be set according to the trader's preferred time zone.
This strategy aims to capitalize on institutional order flow and liquidity patterns in the forex market, providing traders with a systematic approach to identifying potential reversal points with favorable risk-reward profiles.
Overnight Positioning w EMA - Strategy [presentTrading]I've recently started researching Market Timing strategies, and it’s proving to be quite an interesting area of study. The idea of predicting optimal times to enter and exit the market, based on historical data and various indicators, brings a dynamic edge to trading. Additionally, it is integrated with the 3commas bot for automated trade execution.
I'm still working on it. Welcome to share your point of view.
█ Introduction and How it is Different
The "Overnight Positioning with EMA " is designed to capitalize on market inefficiencies during the overnight trading period. This strategy takes a position shortly before the market closes and exits shortly after it opens the following day. What sets this strategy apart is the integration of an optional Exponential Moving Average (EMA) filter, which ensures that trades are aligned with the underlying trend. The strategy provides flexibility by allowing users to select between different global market sessions, such as the US, Asia, and Europe.
It is integrated with the 3commas bot for automated trade execution and has a built-in mechanism to avoid holding positions over the weekend by force-closing positions on Fridays before the market closes.
BTCUSD 20 mins Performance
█ Strategy, How it Works: Detailed Explanation
The core logic of this strategy is simple: enter trades before market close and exit them after market open, taking advantage of potential price movements during the overnight period. Here’s how it works in more detail:
🔶 Market Timing
The strategy determines the local market open and close times based on the selected market (US, Asia, Europe) and adjusts entry and exit points accordingly. The entry is triggered a specific number of minutes before market close, and the exit is triggered a specific number of minutes after market open.
🔶 EMA Filter
The strategy includes an optional EMA filter to help ensure that trades are taken in the direction of the prevailing trend. The EMA is calculated over a user-defined timeframe and length. The entry is only allowed if the closing price is above the EMA (for long positions), which helps to filter out trades that might go against the trend.
The EMA formula:
```
EMA(t) = +
```
Where:
- EMA(t) is the current EMA value
- Close(t) is the current closing price
- n is the length of the EMA
- EMA(t-1) is the previous period's EMA value
🔶 Entry Logic
The strategy monitors the market time in the selected timezone. Once the current time reaches the defined entry period (e.g., 20 minutes before market close), and the EMA condition is satisfied, a long position is entered.
- Entry time calculation:
```
entryTime = marketCloseTime - entryMinutesBeforeClose * 60 * 1000
```
🔶 Exit Logic
Exits are triggered based on a specified time after the market opens. The strategy checks if the current time is within the defined exit period (e.g., 20 minutes after market open) and closes any open long positions.
- Exit time calculation:
exitTime = marketOpenTime + exitMinutesAfterOpen * 60 * 1000
🔶 Force Close on Fridays
To avoid the risk of holding positions over the weekend, the strategy force-closes any open positions 5 minutes before the market close on Fridays.
- Force close logic:
isFriday = (dayofweek(currentTime, marketTimezone) == dayofweek.friday)
█ Trade Direction
This strategy is designed exclusively for long trades. It enters a long position before market close and exits the position after market open. There is no shorting involved in this strategy, and it focuses on capturing upward momentum during the overnight session.
█ Usage
This strategy is suitable for traders who want to take advantage of price movements that occur during the overnight period without holding positions for extended periods. It automates entry and exit times, ensuring that trades are placed at the appropriate times based on the market session selected by the user. The 3commas bot integration also allows for automated execution, making it ideal for traders who wish to set it and forget it. The strategy is flexible enough to work across various global markets, depending on the trader's preference.
█ Default Settings
1. entryMinutesBeforeClose (Default = 20 minutes):
This setting determines how many minutes before the market close the strategy will enter a long position. A shorter duration could mean missing out on potential movements, while a longer duration could expose the position to greater price fluctuations before the market closes.
2. exitMinutesAfterOpen (Default = 20 minutes):
This setting controls how many minutes after the market opens the position will be exited. A shorter exit time minimizes exposure to market volatility at the open, while a longer exit time could capture more of the overnight price movement.
3. emaLength (Default = 100):
The length of the EMA affects how the strategy filters trades. A shorter EMA (e.g., 50) reacts more quickly to price changes, allowing more frequent entries, while a longer EMA (e.g., 200) smooths out price action and only allows entries when there is a stronger underlying trend.
The effect of using a longer EMA (e.g., 200) would be:
```
EMA(t) = +
```
4. emaTimeframe (Default = 240):
This is the timeframe used for calculating the EMA. A higher timeframe (e.g., 360) would base entries on longer-term trends, while a shorter timeframe (e.g., 60) would respond more quickly to price movements, potentially allowing more frequent trades.
5. useEMA (Default = true):
This toggle enables or disables the EMA filter. When enabled, trades are only taken when the price is above the EMA. Disabling the EMA allows the strategy to enter trades without any trend validation, which could increase the number of trades but also increase risk.
6. Market Selection (Default = US):
This setting determines which global market's open and close times the strategy will use. The selection of the market affects the timing of entries and exits and should be chosen based on the user's preference or geographic focus.
Fibonacci Swing Trading BotStrategy Overview for "Fibonacci Swing Trading Bot"
Strategy Name: Fibonacci Swing Trading Bot
Version: Pine Script v5
Purpose: This strategy is designed for swing traders who want to leverage Fibonacci retracement levels and candlestick patterns to enter and exit trades on higher time frames.
Key Components:
1. Multiple Timeframe Analysis:
The strategy uses a customizable timeframe for analysis. You can choose between 4hour, daily, weekly, or monthly time frames to fit your preferred trading horizon. The high and low-price data is retrieved from the selected timeframe to identify swing points.
2. Fibonacci Retracement Levels:
The script calculates two key Fibonacci retracement levels:
0.618: A common level where price often retraces before resuming its trend.
0.786: A deeper retracement level, often used to identify stronger support/resistance areas.
These levels are dynamically plotted on the chart based on the highest high and lowest low over the last 50 bars of the selected timeframe.
3. Candlestick Based Entry Signals:
The strategy uses candlestick patterns as the only indicator for trade entries:
Bullish Candle: A green candle (close > open) that forms between the 0.618 retracement level and the swing high.
Bearish Candle: A red candle (close < open) that forms between the 0.786 retracement level and the swing low.
When these candlestick patterns align with the Fibonacci levels, the script triggers buy or sell signals.
4. Risk Management:
Stop Loss: The stop loss is set at 1% below the entry price for long trades and 1% above the entry price for short trades. This tight risk management ensures controlled losses.
Take Profit: The strategy uses a 2:1 risk-to-reward ratio. The take profit is automatically calculated based on this ratio relative to the stop loss.
5. Buy/Sell Logic:
Buy Signal: Triggered when a bullish candle forms above the 0.618 retracement level and below the swing high. The bot then places a long position.
Sell Signal: Triggered when a bearish candle forms below the 0.786 retracement level and above the swing low. The bot then places a short position.
The stop loss and take profit levels are automatically managed once the trade is placed.
Strengths of This Strategy:
Swing Trading Focus: The strategy is ideal for swing traders, targeting longer-term price moves that can take days or weeks to play out.
Simple Yet Effective Indicators: By only relying on Fibonacci retracement levels and basic candlestick patterns, the strategy avoids complexity while capitalizing on well-known support and resistance zones.
Automated Risk Management: The built-in stop loss and take profit mechanism ensures trades are protected, adhering to a strict 2:1 risk/reward ratio.
Multiple Timeframe Analysis: The script adapts to various market conditions by allowing users to switch between different timeframes (4hour, daily, weekly, monthly), giving traders flexibility.
Strategy Use Cases:
Retracement Traders: Traders who focus on entering the market at key retracement levels (0.618 and 0.786) will find this strategy especially useful.
Trend Reversal Traders: The strategy’s reliance on candlestick formations at Fibonacci levels helps traders spot potential reversals in price trends.
Risk Conscious Traders: With its 1% risk per trade and 2:1 risk/reward ratio, the strategy is ideal for traders who prioritize risk management in their trades.
Dual Chain StrategyDual Chain Strategy - Technical Overview
How It Works:
The Dual Chain Strategy is a unique approach to trading that utilizes Exponential Moving Averages (EMAs) across different timeframes, creating two distinct "chains" of trading signals. These chains can work independently or together, capturing both long-term trends and short-term price movements.
Chain 1 (Longer-Term Focus):
Entry Signal: The entry signal for Chain 1 is generated when the closing price crosses above the EMA calculated on a weekly timeframe. This suggests the start of a bullish trend and prompts a long position.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Exit Signal: The exit signal is triggered when the closing price crosses below the EMA on a daily timeframe, indicating a potential bearish reversal.
exitLongChain1 = enableChain1 and ta.crossunder(src1, exitEMA1)
Parameters: Chain 1's EMA length is set to 10 periods by default, with the flexibility for user adjustment to match various trading scenarios.
Chain 2 (Shorter-Term Focus):
Entry Signal: Chain 2 generates an entry signal when the closing price crosses above the EMA on a 12-hour timeframe. This setup is designed to capture quicker, shorter-term movements.
bullishChain2 = enableChain2 and ta.crossover(src2, entryEMA2)
Exit Signal: The exit signal occurs when the closing price falls below the EMA on a 9-hour timeframe, indicating the end of the shorter-term trend.
exitLongChain2 = enableChain2 and ta.crossunder(src2, exitEMA2)
Parameters: Chain 2's EMA length is set to 9 periods by default, and can be customized to better align with specific market conditions or trading strategies.
Key Features:
Dual EMA Chains: The strategy's originality shines through its dual-chain configuration, allowing traders to monitor and react to both long-term and short-term market trends. This approach is particularly powerful as it combines the strengths of trend-following with the agility of momentum trading.
Timeframe Flexibility: Users can modify the timeframes for both chains, ensuring the strategy can be tailored to different market conditions and individual trading styles. This flexibility makes it versatile for various assets and trading environments.
Independent Trade Logic: Each chain operates independently, with its own set of entry and exit rules. This allows for simultaneous or separate execution of trades based on the signals from either or both chains, providing a robust trading system that can handle different market phases.
Backtesting Period: The strategy includes a configurable backtesting period, enabling thorough performance assessment over a historical range. This feature is crucial for understanding how the strategy would have performed under different market conditions.
time_cond = time >= startDate and time <= finishDate
What It Does:
The Dual Chain Strategy offers traders a distinctive trading tool that merges two separate EMA-based systems into one cohesive framework. By integrating both long-term and short-term perspectives, the strategy enhances the ability to adapt to changing market conditions. The originality of this script lies in its innovative dual-chain design, providing traders with a unique edge by allowing them to capitalize on both significant trends and smaller, faster price movements.
Whether you aim to capture extended market trends or take advantage of more immediate price action, the Dual Chain Strategy provides a comprehensive solution with a high degree of customization and strategic depth. Its flexibility and originality make it a valuable tool for traders seeking to refine their approach to market analysis and execution.
How to Use the Dual Chain Strategy
Step 1: Access the Strategy
Add the Script: Start by adding the Dual Chain Strategy to your TradingView chart. You can do this by searching for the script by name or using the link provided.
Select the Asset: Apply the strategy to your preferred trading pair or asset, such as #BTCUSD, to see how it performs.
Step 2: Configure the Settings
Enable/Disable Chains:
The strategy is designed with two independent chains. You can choose to enable or disable each chain depending on your trading style and the market conditions.
enableChain1 = input.bool(true, title='Enable Chain 1')
enableChain2 = input.bool(true, title='Enable Chain 2')
By default, both chains are enabled. If you prefer to focus only on longer-term trends, you might disable Chain 2, or vice versa if you prefer shorter-term trades.
Set EMA Lengths:
Adjust the EMA lengths for each chain to match your trading preferences.
Chain 1: The default EMA length is 10 periods. This chain uses a weekly timeframe for entry signals and a daily timeframe for exits.
len1 = input.int(10, minval=1, title='Length Chain 1 EMA', group="Chain 1")
Chain 2: The default EMA length is 9 periods. This chain uses a 12-hour timeframe for entries and a 9-hour timeframe for exits.
len2 = input.int(9, minval=1, title='Length Chain 2 EMA', group="Chain 2")
Customize Timeframes:
You can customize the timeframes used for entry and exit signals for both chains.
Chain 1:
Entry Timeframe: Weekly
Exit Timeframe: Daily
tf1_entry = input.timeframe("W", title='Chain 1 Entry Timeframe', group="Chain 1")
tf1_exit = input.timeframe("D", title='Chain 1 Exit Timeframe', group="Chain 1")
Chain 2:
Entry Timeframe: 12 Hours
Exit Timeframe: 9 Hours
tf2_entry = input.timeframe("720", title='Chain 2 Entry Timeframe (12H)', group="Chain 2")
tf2_exit = input.timeframe("540", title='Chain 2 Exit Timeframe (9H)', group="Chain 2")
Set the Backtesting Period:
Define the period over which you want to backtest the strategy. This allows you to see how the strategy would have performed historically.
startDate = input.time(timestamp('2015-07-27'), title="StartDate")
finishDate = input.time(timestamp('2026-01-01'), title="FinishDate")
Step 3: Analyze the Signals
Understand the Entry and Exit Signals:
Buy Signals: When the price crosses above the entry EMA, the strategy generates a buy signal.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Sell Signals: When the price crosses below the exit EMA, the strategy generates a sell signal.
bearishChain2 = enableChain2 and ta.crossunder(src2, entryEMA2)
Review the Visual Indicators:
The strategy plots buy and sell signals on the chart with labels for easy identification:
BUY C1/C2 for buy signals from Chain 1 and Chain 2.
SELL C1/C2 for sell signals from Chain 1 and Chain 2.
This visual aid helps you quickly understand when and why trades are being executed.
Step 4: Optimize the Strategy
Backtest Results:
Review the strategy’s performance over the backtesting period. Look at key metrics like net profit, drawdown, and trade statistics to evaluate its effectiveness.
Adjust the EMA lengths, timeframes, and other settings to see how changes affect the strategy’s performance.
Customize for Live Trading:
Once satisfied with the backtest results, you can apply the strategy settings to live trading. Remember to continuously monitor and adjust as needed based on market conditions.
Step 5: Implement Risk Management
Use Realistic Position Sizing:
Keep your risk exposure per trade within a comfortable range, typically between 1-2% of your trading capital.
Set Alerts:
Set up alerts for buy and sell signals, so you don’t miss trading opportunities.
Paper Trade First:
Consider running the strategy in a paper trading account to understand its behavior in real market conditions before committing real capital.
This dual-layered approach offers a distinct advantage: it enables the strategy to adapt to varying market conditions by capturing both broad trends and immediate price action without one chain's activity impacting the other's decision-making process. The independence of these chains in executing transactions adds a level of sophistication and flexibility that is rarely seen in more conventional trading systems, making the Dual Chain Strategy not just unique, but a powerful tool for traders seeking to navigate complex market environments.
WODIsMA Strategy 3 MA Crossover & Bull-Bear Trend ConfirmationWODIsMA Strategy is a versatile trading strategy designed to leverage the strength of moving averages and volatility indicators to provide clear trading signals for both long and short positions. This strategy is suitable for traders looking for a systematic approach to trading with adjustable parameters to fit various market conditions and personal trading styles.
Key Features
Customizable Moving Averages:
The strategy allows users to select different types of moving averages (SMA, EMA, SMMA, WMA, VWMA) for short-term, mid-term, long-term, and bull-bear trend identification.
Each moving average can be customized with different lengths, sources (e.g., close, high, low), timeframes, and colors.
Position Management:
Users can specify the percentage of capital to use per trade and the percentage to close per partial exit.
The strategy supports both long and short positions with the ability to enable or disable each direction.
Volatility Filter:
Incorporates a volatility filter to ensure trades are only taken when market volatility is above a user-defined threshold, enhancing the strategy's effectiveness in dynamic market conditions.
Bull-Bear Trend Line:
Option to enable a bull-bear trend line that helps identify the overall market trend. Trades are taken based on the relationship between the long-term moving average and the bull-bear trend line.
Partial Exits and Full Close Logic:
The strategy includes logic for partial exits based on the crossing of mid-term and long-term moving averages.
Ensures that positions are fully closed when adverse conditions are detected, such as the price crossing below the bull-bear trend line.
Stop Loss Management:
Implements user-defined stop loss levels to manage risk effectively. The stop loss is dynamically adjusted based on the entry price and user input.
Detailed Description
Moving Average Calculation: The strategy calculates up to six different moving averages, each with customizable parameters. These moving averages help identify the short-term, mid-term, long-term trends, and overall market direction.
Trading Signals:
Long Signal: A long position is opened when the short-term moving average is above the long-term moving average, and the mid-term moving average crosses above the long-term moving average.
Short Signal: A short position is opened when the short-term moving average is below the long-term moving average, and the mid-term moving average crosses below the long-term moving average.
Volatility Condition: The strategy includes a volatility filter that activates trades only when volatility exceeds a specified threshold, ensuring trades are made in favorable market conditions.
Bull-Bear Trend Confirmation: When enabled, trades are filtered based on the relationship between the long-term moving average and the bull-bear trend line, adding another layer of confirmation.
Stop Loss and Exits:
The strategy manages risk by placing stop loss orders based on user-defined percentages.
Positions are partially or fully closed based on the crossing of moving averages and the relationship with the bull-bear trend line.
Originality and Usefulness
This strategy is original as it combines multiple moving averages and volatility indicators in a structured manner to provide reliable trading signals. Its versatility allows traders to adjust the parameters to match their trading preferences and market conditions. The inclusion of a volatility filter and bull-bear trend line adds significant value by reducing false signals and ensuring trades are taken in the direction of the overall market trend. The detailed descriptions and customizable settings make this strategy accessible and understandable for traders, even those unfamiliar with the underlying Pine Script code.
By providing clear entry, exit, and risk management rules, the WODIsMA Strategy enhances the trader's ability to navigate different market environments, making it a valuable addition to the TradingView community scripts.
AlgoBuilder [Mean-Reversion] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely and trade based on historical and backtested data using automation.
The main goal is to build profitable mean-reversion strategies that outperform the underlying asset in terms of returns while minimizing drawdown.
For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based moving averages and bands mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability function for traders who want to implement probabilities right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, profit factor, average trade, average risk-reward ratio (RR), and more.
This aids optimization to meet specific goals and risk tolerance levels effectively.
-----
How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading:
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on mean-reversion and risk per trade approach.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 10% of equity to buy the asset)
-----
What's is FRMA? How does the triple bands work? What are the underlying calculations?
Middle Band (FRMA):
The middle band is the core of the FRMA system. It represents the Fractalyst Moving Average, calculated by identifying the most recent external swing highs and lows in the market structure.
By determining these external swing pivot points, which act as significant highs and lows within the market range, the FRMA provides a unique moving average that adapts to market structure changes.
Upper Band:
The upper band shows the average price of the most recent external swing highs.
External swing highs are identified as the highest points between pivot points in the market structure.
This band helps traders identify potential overbought conditions when prices approach or exceed this upper band.
Lower Band:
The lower band shows the average price of the most recent external swing lows.
External swing lows are identified as the lowest points between pivot points in the market structure.
The script utilizes this band to identify potential oversold conditions, triggering entry signals as prices approach or drop below the lower band.
Adjustments Based on User Inputs:
Users can adjust how the upper and lower bands are calculated based on their preferences:
Upper/Lower: This method calculates the average bands using the prices of external swing highs and lows identified in the market.
Percentage Deviation from FRMA: Alternatively, users can opt to calculate the bands based on a percentage deviation from the middle FRMA. This approach provides flexibility to adjust the width of the bands relative to market conditions and volatility.
-----
What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
⍺: MA Period | Σ: MA Timeframe
-----
What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
2. Hunt Entries :
- The strategy identifies a candle that wicks through the lower FRMA band.
- It waits for the next candle to close above the low of the wick candle.
- When this condition is met and the bar is closed, the strategy takes the buy entry.
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 2
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (20) * 2
⍺: ADR period | Σ: ADR Multiplier
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
Application in Strategy (ATR/ADR):
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
Example:
If the entry price is $100, the initial risk is $10, and the RR ratio is 2, the break-even level is $100 + ($10 * 2) = $120.
FRMA Based:
Moves the stop-loss to break-even when the price hits the FRMA level at which the entry was taken.
Calculation:
Break-even level = FRMA level at the entry
Example:
If the FRMA level at entry is $102, the break-even level is set to $102 when the price reaches $102.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
When Both Percentage (%) Based and RR Based Take Profit Levels Are Off:
The script will adjust the take profit level to the higher FRMA band set within user inputs.
Calculation:
Take profit level = Higher FRMA band length/timeframe specified by the user.
This ensures that when neither percentage-based nor risk-to-reward-based take profit methods are enabled, the strategy defaults to using the higher FRMA band as the take profit level, providing a consistent and structured approach to profit-taking.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 55%
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 1%
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Utilizing built-in market structure-based moving averages across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Power Hour Money StrategyDescription of the Pine Script Code: "Power Hour Money Strategy"
This Pine Script strategy, "Power Hour Money Strategy," is designed to trade based on the alignment of multiple time frames (month, week, day, and hour). The strategy aims to enter long or short positions depending on whether all selected time frames are in sync (all green for long positions, all red for short positions). Additionally, the script includes configurations for trading during specific sessions and automatically closing positions at the end of the trading day.
Core Features:
1. Time Frame Sync Check:
- The strategy evaluates whether the current price is higher than the opening price for the month, week, day, and hour to determine if each time frame is "green" (bullish) or "red" (bearish).
2. Session Control:
- The user can select between different trading sessions:
- "NY Session 9:30-11:30"
- "Extended NY Session 8-4"
- "All Sessions"
- Trades are only executed if the current time falls within the selected session.
3. Trailing Stop Mechanism:
- The strategy includes an optional trailing stop mechanism for both long and short positions.
- The trailing stop is configured with a percentage loss from the current price to protect gains.
4. End-of-Day Position Management:
- An option is provided to automatically close all positions at the end of the trading day (5:45 PM Eastern Time).
Detailed Code Breakdown:
1. Input Settings:
- **Session Selection**: Allows the user to choose the trading session.
- **End-of-Day Close**: Option to automatically close positions at the end of the day.
- **Trailing Stop Loss**: Enables or disables the trailing stop loss feature and sets the percentage for long and short positions.
2. Time Frame Calculations:
- The script uses `request.security` to get the opening prices for higher time frames (monthly, weekly, daily, and hourly).
- It compares the current close price to these opening prices to determine if each time frame is green or red.
3. Session Time Definitions:
- Defines the start and end times for the NY session (9:30-11:30 AM) and the extended session (8:00 AM - 4:00 PM).
4. Trade Execution:
- The strategy checks if all selected time frames are in sync and if the current time falls within the trading session.
- If all conditions are met, it enters a long or short position.
5. Trailing Stop Loss Implementation:
- Adjusts the stop price based on the trailing percentage and the current position's size.
- Automatically exits positions if the trailing stop condition is met.
6. End-of-Day Close Implementation:
- Uses a timestamp to check if the current time is 5:45 PM Eastern Time.
- Closes all positions if the end-of-day condition is met.
7. Plotting and Logging:
- Plots indicators to visualize the green/red status of each time frame.
- Logs information about the status of each time frame for debugging and analysis.
Example Usage:
Entering a Long Position: If the month, week, day, and hour are all green and the current time is within the selected session, a long position is entered.
Entering a Short Position: If the month, week, day, and hour are all red and the current time is within the selected session, a short position is entered.
Trailing Stop: Protects gains by exiting the position if the price moves against the set trailing stop percentage.
End-of-Day Close: Automatically closes all open positions at 5:45 PM Eastern Time if enabled.
This strategy is particularly useful for traders who want to ensure that multiple time frames are in alignment before entering a trade and who wish to manage positions effectively throughout the trading day with specific session controls and trailing stops.
AlgoBuilder [Trend-Following] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely on and trade based on historical and backtested data using automation. The main goal is to build profitable trend-following strategies that outperform the underlying asset in terms of returns while minimizing drawdown. For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based trailing stop-loss mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability and sentiment function for traders who want to implement probabilities and market sentiment right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, compound annual growth rate (CAGR), profit factor, average trade, average risk-reward ratio (RR), and more. This aids optimization to meet specific goals and risk tolerance levels effectively.
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How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading (1x):
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on trend-following and risk management.
- (1x) This mode ensures no stacking of positions, allowing for only one running position or trade at a time.
◓: Mode | %: Risk percentage per trade
2. Trading (2x):
Similar to the 1x mode but allows for two pyramiding entries.
This approach enables traders to increase their position size as the trade moves in their favor, potentially enhancing profits during strong bullish trends.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes 100% of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 100% of equity to buy the asset)
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>/<) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
MA #1: Fast MA | MA #2: Medium MA | MA #3: Slow MA
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 1.5
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (14) * 1.5
⍺: ADR period | Σ: ADR Multiplier
Application in Strategy:
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Trailing Stop-Loss:
One of the key elements of this strategy is its ability to detec buyside and sellside liquidity levels across multiple timeframes to trail the stop-loss once the trade is in running profits.
By utilizing this approach, the strategy allows enough room for price to run.
There are two built-in trailing stop-loss (SL) options you can choose from while in a trade:
1. External Trailing Stop-Loss:
- Uses sell-side liquidity to trail your stop-loss, allowing price to consolidate before continuation. This method is less aggressive and provides more room for price fluctuations.
Example - External - Wick below the trailing SL - 12H trailing timeframe
⍺: Exit type | Σ: Trailing stop-loss timeframe
2. Internal Trailing Stop-Loss:
- Uses the most recent swing low with a period of 2 to trail your stop-loss. This method is more aggressive compared to the external trailing stop-loss, as it tightens the stop-loss closer to the current price action.
Example - Internal - Close below the trailing SL - 6H trailing timeframe
⍺: Exit type | Σ: Trailing stop-loss timeframe
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
- You can choose to set a break-even level at which your initial stop-loss moves to the entry price as soon as it hits, and your trailing stop-loss gets activated (if enabled).
- You can select either a percentage (%) or risk-to-reward (RR) based break-even, allowing you to set your break-even level as a percentage amount above the entry price or based on RR.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
The underlying calculations involve determining the price levels at which these actions are triggered. For break-even, it moves the initial stop-loss to the entry price and activate the trailing stop-loss once the break-even level is reached.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 50%
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What's the sentiment Filter? What are the underlying calculations?
Sentiment filter aims to calculate the percentage level of bullish or bearish fluctuations within equally divided price sections, in the latest price range.
Calculations:
This filter calculates the current sentiment by identifying the highest swing high and the lowest swing low, then evenly dividing the distance between them into percentage amounts. If the price is above the 50% mark, it indicates bullishness, whereas if it's below 50%, it suggests bearishness.
Sentiment Bias Identification:
Bullish Bias: The current price is trading above the 50% daily range.
Bearish Bias: The current price is trading below the 50% daily range.
Example - Sentiment Enabled | Bullish degree above 50% | Bullish sentimental bias
>: Minimum required sentiment for entry | %: Current sentimental degree in a (Bullish/Bearish) sentimental bias
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 5% | Price must be in a bearish range
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades, Compound Annual Growth Rate (CAGR), MAR and more.
CAGR: It calculates the 'Compound Annual Growth Rate' first and last taken trades on your chart. The CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two strategies. Since it annualizes values, it requires a minimum 4H timeframe to display the CAGR value. annualizing returns over smaller periods of times doesn't produce very meaningful figures.
MAR: Measure of return adjusted for risk: CAGR divided by Max Drawdown. Indicates how comfortable the system might be to trade. Higher than 0.5 is ideal, 1.0 and above is very good, and anything above 3.0 should be considered suspicious and you need to make sure the total number of trades are high enough by running a Deep Backtest in strategy tester. (available for TradingView Premium users.)
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most trend-following successful strategies have a percent profitability of 15-40% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Labels:
- OFF: Hides labels in the performance table.
- PnL: Shows the profit and loss of each trade individually, providing detailed insights into the performance of each trade.
- Range: Shows the range length and Average Day Range (ADR), offering additional context about market conditions during each trade.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, MAR (Mar Ratio), CAGR (Compound Annual Growth Rate), and net profit with minimum drawdown. Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Leveraging market sentiment to construct a profitable approach.
3. Utilizing built-in market structure-based trailing stop-loss mechanisms across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Strategy Properties
This script backtest is done on 4H COINBASE:BTCUSD , using the following backtesting properties:
Balance: $5000
Order Size: 10% of the equity
Risk % per trade: 1%
Commission: 0.04% (Default commission percentage according to TradingView competitions rules)
Slippage: 75 ticks
Pyramiding: 2
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Multi Timeframe RSI Buy Sell Strategy [TradeDots]The "Multi Timeframe RSI Buy/Sell Strategy" is a trading strategy that utilizes Relative Strength Index (RSI) indicators from multiple timeframes to provide buy and sell signals.
This strategy allows for extensive customization, supporting up to three distinct RSIs, each configurable with its own timeframe, length, and data source.
HOW DOES IT WORK
This strategy integrates up to three RSIs, each selectable from different timeframes and customizable in terms of length and source. Users have the flexibility to define the number of active RSIs. These selections visualize as plotted lines on the chart, enhancing interpretability.
Users can also manage the moving average of the selected RSI lines. When multiple RSIs are active, the moving average is calculated based on these active lines' average value.
The color intensity of the moving average line changes as it approaches predefined buying or selling thresholds, alerting users to potential signal generation.
A buy or sell signal is generated when all active RSI lines simultaneously cross their respective threshold lines. Concurrently, a label will appear on the chart to signify the order placement.
For those preferring not to display order information or activate the strategy, an "Enable backtest" option is provided in the settings for toggling activation.
APPLICATION
The strategy leverages multiple RSIs to detect extreme market conditions across various timeframes without the need for manual timeframe switching.
This feature is invaluable for identifying divergences across timeframes, such as detecting potential short-term reversals within broader trends, thereby aiding traders in making better trading decisions and potentially avoiding losses.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 60%
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
TradeDots Stochastic Z-Score
Kyrie Crossover ( @zaytradellc )Unlocking Market Dynamics: Kyrie Crossover Script by @zaytradellc
personalized trading success with the "Kyrie Crossover" script, meticulously crafted by @zaytrade. This innovative Pine Script, tailored to the birthdays of Kyrie and the script creator, combines the power of technical analysis with a touch of personalization to revolutionize your trading experience.
**Exponential Moving Average (EMA) Crossover Strategy:**
At the heart of the "Kyrie Crossover" script lies a sophisticated EMA crossover strategy. By utilizing a 10-period EMA and a 323-period EMA (symbolizing long term price action ), the strategy effectively captures market trends with precision and insight.
- **Short-Term EMA (10-period):** This EMA reacts swiftly to recent price changes, offering heightened sensitivity to short-term fluctuations. It excels in identifying immediate shifts in market sentiment, making it invaluable for pinpointing short-lived trends and potential reversal points.
- **Long-Term EMA (323-period):** In contrast, the long-term EMA provides a broader perspective by smoothing out short-term noise and focusing on longer-term trend direction. Its extended length filters out market noise effectively, providing a clear representation of the underlying trend's momentum and sustainability.
**Directional Movement Index (DMI) Metrics:**
The "Kyrie Crossover" script goes beyond traditional indicators by incorporating DMI metrics across multiple timeframes. By assessing trend strength and direction, traders gain valuable insights into market dynamics, allowing for informed decision-making.
**Simple Instructions to Profit:**
1. **Identify EMA Crossovers:** Look for instances where the short-term EMA (10-period) crosses above the long-term EMA (323-period) for a bullish signal, indicating a potential buying opportunity. Conversely, a crossover where the short-term EMA crosses below the long-term EMA signals a bearish trend and a potential selling opportunity.
2. **Confirm with DMI Metrics:** Validate EMA crossovers by checking DMI metrics across different timeframes (5 minutes, 15 minutes, 30 minutes, and 1 hour). Pay attention to color-coded indicators, with green indicating a bullish trend, red indicating a bearish trend, and white indicating no clear trend.
3. **Manage Risk:** Implement proper risk management techniques, such as setting stop-loss orders and position sizing based on your risk tolerance and trading objectives.
4. **Stay Informed:** Regularly monitor market conditions and adjust your trading strategy accordingly based on new signals and emerging trends.
Bitcoin Momentum StrategyThis is a very simple long-only strategy I've used since December 2022 to manage my Bitcoin position.
I'm sharing it as an open-source script for other traders to learn from the code and adapt it to their liking if they find the system concept interesting.
General Overview
Always do your own research and backtesting - this script is not intended to be traded blindly (no script should be) and I've done limited testing on other markets beyond Ethereum and BTC, it's just a template to tweak and play with and make into one's own.
The results shown in the strategy tester are from Bitcoin's inception so as to get a large sample size of trades, and potential returns have diminished significantly as BTC has grown to become a mega cap asset, but the script includes a date filter for backtesting and it has still performed solidly in recent years (speaking from personal experience using it myself - DYOR with the date filter).
The main advantage of this system in my opinion is in limiting the max drawdown significantly versus buy & hodl. Theoretically much better returns can be made by just holding, but that's also a good way to lose 70%+ of your capital in the inevitable bear markets (also speaking from experience).
In saying all of that, the future is fundamentally unknowable and past results in no way guarantee future performance.
System Concept:
Capture as much Bitcoin upside volatility as possible while side-stepping downside volatility as quickly as possible.
The system uses a simple but clever momentum-style trailing stop technique I learned from one of my trading mentors who uses this approach on momentum/trend-following stock market systems.
Basically, the system "ratchets" up the stop-loss to be much tighter during high bearish volatility to protect open profits from downside moves, but loosens the stop loss during sustained bullish momentum to let the position ride.
It is invested most of the time, unless BTC is trading below its 20-week EMA in which case it stays in cash/USDT to avoid holding through bear markets. It only trades one position (no pyramiding) and does not trade short, but can easily be tweaked to do whatever you like if you know what you're doing in Pine.
Default parameters:
HTF: Weekly Chart
EMA: 20-Period
ATR: 5-period
Bar Lookback: 7
Entry Rule #1:
Bitcoin's current price must be trading above its higher-timeframe EMA (Weekly 20 EMA).
Entry Rule #2:
Bitcoin must not be in 'caution' condition (no large bearish volatility swings recently).
Enter at next bar's open if conditions are met and we are not already involved in a trade.
"Caution" Condition:
Defined as true if BTC's recent 7-bar swing high minus current bar's low is > 1.5x ATR, or Daily close < Daily 20-EMA.
Trailing Stop:
Stop is trailed 1 ATR from recent swing high, or 20% of ATR if in caution condition (ie. 0.2 ATR).
Exit on next bar open upon a close below stop loss.
I typically use a limit order to open & exit trades as close to the open price as possible to reduce slippage, but the strategy script uses market orders.
I've never had any issues getting filled on limit orders close to the market price with BTC on the Daily timeframe, but if the exchange has relatively low slippage I've found market orders work fine too without much impact on the results particularly since BTC has consistently remained above $20k and highly liquid.
Cost of Trading:
The script uses no leverage and a default total round-trip commission of 0.3% which is what I pay on my exchange based on their tier structure, but this can vary widely from exchange to exchange and higher commission fees will have a significantly negative impact on realized gains so make sure to always input the correct theoretical commission cost when backtesting any script.
Static slippage is difficult to estimate in the strategy tester given the wide range of prices & liquidity BTC has experienced over the years and it largely depends on position size, I set it to 150 points per buy or sell as BTC is currently very liquid on the exchange I trade and I use limit orders where possible to enter/exit positions as close as possible to the market's open price as it significantly limits my slippage.
But again, this can vary a lot from exchange to exchange (for better or worse) and if BTC volatility is high at the time of execution this can have a negative impact on slippage and therefore real performance, so make sure to adjust it according to your exchange's tendencies.
Tax considerations should also be made based on short-term trade frequency if crypto profits are treated as a CGT event in your region.
Summary:
A simple, but effective and fairly robust system that achieves the goals I set for it.
From my preliminary testing it appears it may also work on altcoins but it might need a bit of tweaking/loosening with the trailing stop distance as the default parameters are designed to work with Bitcoin which obviously behaves very differently to smaller cap assets.
Good luck out there!
Inside Candle StrategyIntroduction
The Inside Candle Breakout Strategy leverages the concept of inside candles as a primary signal for potential breakouts. Unlike common trend-following or scalping strategies, this method focuses on the volatility squeeze indicated by inside candles and aims to capture the momentum that follows these periods of consolidation. The strategy's originality lies in its specific integration of timeframes for signal detection and its application across diverse market conditions without relying on conventional trend indicators.
Strategy Description and Mechanics
Inside Candle Identification: At the heart of this strategy is the detection of inside candles, defined as candles fully contained within the range of the preceding candle. This pattern signifies a temporary balance between buyers and sellers, often preceding significant price movements. The strategy scans for these candles within a user-specified timeframe in the input section of the settings of the strategy, allowing for tailored signal generation based on individual trading preferences.
Entry Points and Market Entries: Upon identifying an inside candle and only once this candle closes, the strategy prepares to enter a trade in the direction of the breakout. Trades are executed in the timeframe selected on the chart, ensuring that entry points are aligned with real-time market movements. This process highlights the strategy's adaptability, making it suitable for various trading styles, from day trading to swing trading.
Overlay Indicator for Enhanced Market Analysis: Accompanying the breakout signals is an overlay indicator comprising two moving averages and a volatility cloud. This feature serves as a secondary tool for market analysis, offering insights into the prevailing market trend and volatility levels. While it doesn't influence the entry or exit signals directly, it provides traders with additional context for refining their decisions, enhancing the strategy's utility. This assistance tool is composed by one moving average and a second line which is calculated adding or subtracting the historical volatility of the asset on the moving average, depending on his momentum.
Strategy Results and Commitment to Realism
Backtesting Protocol: In our commitment to transparency and realism, backtesting results are derived from a dataset that ensures a sufficient number of trades (over 100) to validate the strategy's effectiveness. This approach underscores our dedication to providing traders with reliable and actionable insights.
Risk Management and Trade Sizing: Recognizing the importance of sustainable trading practices, the strategy incorporates strict risk management guidelines. Trades are sized to ensure that only a small percentage of equity is risked on a single trade, adhering to widely accepted risk tolerance levels. The initial account size for this script is set to 10000$.
Strategy Defaults and Justification: The default properties of the strategy, including the risk-reward ratio, average length for moving averages, and other parameters, are carefully chosen based on extensive testing and analysis. These settings represent a balanced approach, aiming to optimize the strategy's performance across a variety of market conditions.
Strategy Components:
- Inside Candles: An inside candle occurs when a candle's high and low are completely contained within the high and low of the previous candle. This pattern indicates a period of consolidation or indecision in the market, often preceding a significant price movement. The strategy detects inside candles based on the user-selected timeframe, allowing traders to capture potential breakouts.
Indicator Overlays:
- Moving Average: A simple moving average (SMA) is calculated over a user-defined length (`Average Length`), providing a dynamic baseline to gauge the market's direction. The strategy offers an option (`Show Moving Average`) to display or hide this moving average on the chart, giving traders control over the visual complexity.
- Volatility Measurement: Alongside the moving average, the strategy assesses market volatility using the standard deviation of the closing prices over the same period defined by the `Average Length`. The moving average is adjusted upwards or downwards by this volatility measure, creating a dynamic channel that reflects the current market conditions.
- Color Gradients for Volatility: The strategy uses a color gradient to fill the area between the moving average and its volatility-adjusted counterpart. This gradient visually represents the volatility level, transitioning from gray (low volatility) to a lighter shade (higher volatility), aiding in the assessment of market sentiment and volatility.
Trading Entries:
- Long Entry: A long position is triggered when the closing price exceeds the high of an inside candle, indicating potential bullish momentum. The strategy places a stop-loss at the low of the inside candle and sets a take-profit level based on the predefined risk-reward ratio (`RR Ratio`).
- Short Entry: Conversely, a short position is initiated when the closing price falls below the low of an inside candle, suggesting bearish pressure. A stop-loss is set at the high of the inside candle, with the take-profit level adjusted according to the risk-reward ratio.
Customization Settings:
- Timeframe: Traders can select the desired timeframe for inside candle detection, tailoring the strategy to fit various trading styles and time horizons.
- RR Ratio: The risk-reward ratio is adjustable, allowing traders to manage the potential risk and return of each trade according to their risk tolerance.
- Average Length: This setting determines the period over which the moving average and volatility are calculated, affecting the sensitivity of the strategy to price movements.
- Visual Settings: Users can customize the appearance of the strategy on their charts, including the colors of the moving average and volatility lines, as well as the line width, enhancing chart readability and personal preference adherence.
Disclaimer
Trading involves significant risk, and it is crucial for traders to conduct their own due diligence before engaging with any strategy. The Inside Candle Breakout Strategy is presented for informational purposes only and does not constitute financial advice.
Crypto Punk [Bot] (Zeiierman)█ Overview
The Crypto Punk (Zeiierman) is a trading strategy designed for the dynamic and volatile cryptocurrency market. It utilizes algorithms that incorporate price action analysis and principles inspired by Geometric Brownian Motion (GBM). The bot's core functionality revolves around analyzing differences in high and low prices over various timeframes, estimating drift (trend) and volatility, and applying this information to generate trading signals.
█ How to use the Crypto Punk Bot
Utilize the Crypto Punk Bot as a technical analysis tool to enhance your trading strategy. The signals generated by the bot can serve as a confirmation of your existing approach to entering and exiting the market. Additionally, the backtest report provided by the bot is a valuable resource for identifying the optimal settings for the specific market and timeframe you are trading in.
One method is to use the bot's signals to confirm entry points around key support and resistance levels.
█ Key Features
Let's explain how the core features work in the strategy.
⚪ Strategy Filter
The strategy filter plays a vital role in the entries and exits. By setting this filter, the bot can identify higher or lower price points at which to execute trades. Opting for higher values will make the bot target more long-term extreme points, resulting in fewer but potentially more significant signals. Conversely, lower values focus on short-term extreme points, offering more frequent signals focusing on immediate market movements.
How is it calculated?
This filter identifies significant price points within a specified dynamic range by applying linear regression to the absolute deviation of the range, smoothing out fluctuations, and determining the trend direction. The algorithm then normalizes the data and searches for extreme points.
⚪ External AI filter
The external AI filter allows traders to incorporate two external sources as signal filters. This feature is particularly useful for refining their signal accuracy with additional data inputs.
External sources can include any indicator applied to your TradingView chart that produces a plot as an output, such as a moving average, RSI, supertrend, MACD, etc. Traders can use these indicators of their choice to set filters for screening signals within the strategy.
This approach offers traders increased flexibility to select filters that align with their trading style. For instance, one trader might prefer to take trades when the price is above a moving average, while another might opt for trades when the MACD is below the MACD signal line. These external filters enable traders to choose options that best fit their trading strategies. See the example below. Note that the input sources for the External AI filter can be any indicator applied to the chart, and the input source per se does not make this strategy unique. The AI filter takes the selected input source and applies our function to it. So, if a trader selects RSI as an input filter, RSI is not unique, but how the source is computed within the AI functions is.
How is it calculated?
Once the external filters are selected and enabled within the settings panel, our AI function is applied to enhance the filter's ability to execute trades, even when the set conditions of the filter are not met. For instance, if a trader wants to take trades only when the price is above a moving average, the AI filter can actually execute trades even if the price is below the moving average.
The filter works by combining k-nearest Neighbors (KNN) with Geometric Brownian Motion (GBM) involves first using GBM to model the historical price trends of an asset, identifying patterns of drift and volatility. KNN is then applied to compare the current market conditions with historical instances, identifying the closest matches based on similar market behaviors. By examining the drift values of these nearest historical neighbors, KNN predicts the current trend's direction.
The AI adaptability value is a setting that determines how flexible the AI algorithm is when applying the external AI filter. Setting the adaptability to 10 indicates minimal adaptability, suggesting that the bot will strictly adhere to the set filter criteria. On the other hand, a higher adaptability value grants the algorithm more leeway to "think outside the box," allowing it to consider signals that may not strictly meet the filter criteria but are deemed viable trading opportunities by the AI.
█ Examples
In this example, the RSI is used to filter out signals when the RSI is below the smoothing line, indicating that prices are declining.
Note that the external filter is specifically designed to work with either 'LONG ONLY' or 'SHORT ONLY' modes; it does not apply when the bot is set to trade on 'BOTH' modes. For 'LONG ONLY' positions, the filter criteria are met when source 1 is greater than source 2 (source 1 >= source 2). Conversely, for 'SHORT ONLY' positions, the filter criteria require source 1 to be less than source 2 (source 1 <= source 2).
Examples of Filter Usage:
Long Signals: To receive long signals when the closing price is higher than a moving average, set Source 1 to the 'close' price and Source 2 to a moving average value. This setup ensures that signals are generated only when the closing price exceeds the moving average, indicating a potential upward trend.
█ Settings
⚪ Set Timeframe
Choosing the correct entry and exit timeframes is crucial for the bot's performance. The general guideline is to select a timeframe that is higher than the one currently displayed on the trading chart but still relatively close in duration. For instance, if trading on a 1-minute chart, setting the bot's Timeframe to 5 minutes is advisable.
⚪ Entry
Traders have the flexibility to configure the bot according to their trading strategy, allowing them to choose whether the bot should engage in long positions only, short positions only or both. This customization ensures that the bot aligns with the trader's market outlook and risk tolerance.
⚪ Pyramiding
Pyramiding functionality is available to enhance the bot's trading strategy. If the current position experiences a drawdown by a specified number of points, the bot is programmed to add new positions to the existing one, potentially capitalizing on lower prices to average down the entry cost. To utilize this feature, access the settings panel, navigate to 'Properties,' and look for 'Pyramiding' to specify the number of times the bot can re-enter the market (e.g., setting it to 2 allows for two additional entries).
⚪ Risk Management
The bot incorporates several risk management methods, including a regular stop loss, trailing stop, and risk-reward-based stop loss and exit strategies. These features assist traders in managing their risk.
Stop Loss
Trailing Stop
⚪ Trading on specific days
This feature allows trading on specific days by setting which days of the week the bot can execute trades on. It enables traders to tailor their strategies according to market behavior on particular days.
⚪ Alerts
Alerts can be set for entry, exit, and risk management. This feature allows traders to automate their trading strategy, ensuring timely actions are taken according to predefined criteria.
█ How is Crypto Punk calculated?
The Crypto Punk Bot is a trading bot that utilizes a combination of price action analysis and elements inspired by Geometric Brownian Motion (GBM) to generate buy and sell signals for cryptocurrencies. The bot focuses on analyzing the difference between high and low prices over various timeframes, alongside estimates of drift (trend) and volatility derived from GBM principles.
Timeframe Analysis for Price Action
The bot examines multiple timeframes (e.g., daily, weekly) to identify the range between the highest and lowest prices within each period. This range analysis helps in understanding market volatility and the potential for significant price movements. The algorithm calculates the trading range by applying maximum and minimum functions to the set of prices over your selected timeframe. It then subtracts these values to determine the range's width. This method offers a quantitative measure of the asset's price volatility for the specified period.
Estimating Drift (Trend)
The bot estimates the drift component, which reflects the underlying trend or expected return of the cryptocurrency. The algorithm does this by estimating the drift (trend) using Geometric Brownian Motion (GBM), which involves determining an asset's average rate of return over time, reflecting the asset's expected direction of movement.
Estimating Volatility
Volatility is estimated by calculating the standard deviation of the logarithmic returns of the cryptocurrency's price over the same timeframe used for the drift calculation. Geometric Brownian Motion (GBM) involves measuring the extent of variation or dispersion in the returns of an asset over time. In the context of GBM, volatility quantifies the degree to which the price of an asset is expected to fluctuate around its drift.
Combining Drift and Volatility for Signal Generation
The bot uses the calculated drift and volatility to understand the current market conditions. A higher drift coupled with manageable volatility may indicate a strong upward trend, suggesting a potential buy signal. Conversely, a low or negative drift with increasing volatility might suggest a weakening market, triggering a sell signal.
█ Strategy Properties
This script backtest is done on the 1 hour chart Bitcoin, using the following backtesting properties:
Balance (default): 10 000 (default base currency)
Order Size: 10% of the equity
Commission: 0.05 %
Slippage: 500 ticks
Stop Loss: Risk Reward set to 1
These parameters are set to provide an accurate representation of the backtesting environment. It's important to recognize that default settings may vary for several reasons outlined below:
Order Size: The standard is set at one contract to facilitate compatibility with a wide range of instruments, including futures.
Commission: This fee is subject to fluctuation based on the specific market and financial instrument, and as such, there isn't a standard rate that will consistently yield accurate outcomes.
We advise users to customize the Script Properties in the strategy settings to match their personal trading accounts and preferred platforms. This adjustment is crucial for obtaining practical insights from the deployed strategies.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Triple MA HTF strategy - Dynamic SmoothingThe triple MA strategy is a simple but effective method to trade the trend. The advantage of this script over the existing triple MA strategies is that the user can open a lower time frame chart and select higher time frame inputs for different MA types mainting the visibility on the chart. The dynamic smoothing code makes sure the HTF trendlines are not jagged, but a fluid line visiable on the lower time frame chart. The script comes with a MA crossover and crossunder strategy explained below.
Moving Averages (MA) Crossover for Entry:
Long Entry: A long entry signal is triggered when the moving average line 1 crosses above the moving average line 2. This crossover indicates a potential shift in market sentiment towards the upside. However, to validate this signal, the strategy checks if the moving average 3 on a higher time frame (eg. 4 hour) is in an upward trend. This additional filter ensures that the trade aligns with the prevailing trend on a broader time scale, increasing the probability of success.
Short Entry: Conversely, a short entry signal occurs when the moving average line 1 crosses below the moving average line 2. This crossover suggests a possible downturn in market momentum. However, for a short trade to be confirmed, the strategy verifies that the moving average 3 on the higher time frame is in a downward trend. This confirmation ensures that the trade is in harmony with the overarching market direction.
Exit from Long Position: The strategy triggers an exit signal from a long position when the moving average line 1 crosses below the moving average line 2. This crossover indicates a potential reversal in the market trend, prompting the trader to close their long position and take profits or minimize losses.
Exit from Short Position: Similarly, an exit signal from a short position occurs when the moving average line 1 crosses above the moving average line 2. This crossover suggests a potential shift in market sentiment towards the upside, prompting the trader to exit their short position and manage their risk accordingly.
Features of the script
This Triple MA Strategy is basically the HTF Trend Filter displayed 3 times on the chart. For more infomation on how the MA with dynamic smoothing is calculated I recommend reading the following script:
For risk management I included a simple script to opt for % of eauity or # of contracts of in the instrument. For explanation on how the risk management settings work I refer to my ealier published script:
The strategy is a simplified example for setting up an entry and exit logic based on multiple moving avarages. Hence the script is meant for educational purposes only.
MACD + MA HTF Strategy - Dynamic SmoothingMACD + MA HTF Strategy - Dynamic Smoothing
The MACD alone generally gives too many false signals and is therefore often used in combination with different indicators. The basic idea to combine the MACD with a moving average on a higher time frame is a commonly used technique to only enter Longs in a uptrend and only Shorts in a downtrend while trading on lower timeframe charts. However, the main issue in many strategy scripts is that the HTF indicator is not visible on lower timecharts. With this strategy example I used the Dynamic Smoothing code to visualise the HTF MA filter to display on you lower timechart. This way it is easier to optimize the strategy settings to the instrument chart.
Orginality and Usefulness - Dynamic Smoothing
Visualizing a higher time frame on a lower timechart often gives jagged lines on your chart. As the calculation is done on less bars, compared to the bars you have open on your timechart. The dynamic smoothing factor is derived by taking the ratio of minutes of the higher time frame to the current time frame. This ensures the moving average remains fluid and consistent across different time frames, eliminating 'jagged' lines on your chart. This new MA value is then used as HTF filter on top of the MACD entry settings. Always make sure the time chart is equal or lower than the timeframe settings in the configuration settings. The intention of the script is to visualise the higher time frame confirmations while trading on a lower timechart.
Code example how to achieve Dynamic Smoothing:
// Get minutes for current and higher timeframes
// Function to convert a timeframe string to its equivalent in minutes
timeframeToMinutes(tf) =>
multiplier = 1
if (str.endswith(tf, "D"))
multiplier := 1440
else if (str.endswith(tf, "W"))
multiplier := 10080
else if (str.endswith(tf, "M"))
multiplier := 43200
else if (str.endswith(tf, "H"))
multiplier := int(str.tonumber(str.replace(tf, "H", "")))
else
multiplier := int(str.tonumber(str.replace(tf, "m", "")))
multiplier
// Get minutes for current and higher timeframes
currentTFMinutes = timeframeToMinutes(timeframe.period)
higherTFMinutes = timeframeToMinutes(TimeFrame_Trend)
// Calculate the smoothing factor
dynamicSmoothing = math.round(higherTFMinutes / currentTFMinutes)
MA_Value_Smooth = ta.sma(MA_Value_HTF, dynamicSmoothing)
Complete Strategy
The MACD and HTF moving average is used to determine when to enter a new position. However a strategy should consider more factors than only the timing of entering a trade. To complete the strategy I included:
an option to choose from different MA types per indicator
a Risk Management Tool
a Take Profit Logic
a Trailing stop loss
a Hard Stoploss
a visual representation of TP and SL
This is merely an example how to structure a strategy and many different setups are possible.
The features in this script are explained below:
Different MA types
The script supports various MA types like EMA, SMA, DEMA, TEMA, WMA and HMA. You can select the MA type and different timeframe to your liking.
Risk Management Tool
Traders can choose to allocate their position size based on a percentage of equity or a fixed number of contracts. This feature ensures prudent risk management and helps traders align their position sizes with their risk tolerance. In the strategy -0.5 is equal to 50% of equity and 1.5 is 150% of equity used. Make sure to align the % of equity with your maxdrawn results with backtesting. Personally I don't want higher max drawdowns than 15%. For the strategy results the script considers 0.05 commission rate on each trade, to stay conservative. For a more detailed explanation I refer to my earlier published tradingviewblog about risk management:
Take Profit Logic
The take profit logic in this script is designed for optimization, offering three distinct exit levels. Traders can customize these levels based on their risk appetite. The script allows adjustment of both the percentage take profit level and the position size, catering to individual trading strategies and objectives. The default settings closes 33% of the position when TP target is hit. The TP levels are simply calculated by inputting a % of of the entryprice.
Trailing Stop and Hard Stoploss
To mitigate downside risks and protect profits, the script incorporates a well-thought-out trailing stop mechanism based on the Average True Range (ATR). This dynamic trailing stop adapts to market volatility, allowing traders to secure gains while letting profitable positions run. Additionally, to prevent significant losses, a fixed stop loss is implemented, providing an added layer of protection.
Visual Representation of Take Profit and Stoploss Levels
For enhanced visualization, take profit and stoploss levels are displayed on the chart. Take profit levels are depicted with green lines, providing a clear indication of potential exit points. Conversely, the trailing stop loss is presented by the red line, serving as visual cues for risk management. Visualizing indicators makes is easier to optimize settings to your liking.
Ideal Settings and Accessibility
This script is intended to be used on lower timeframe charts like 10 to 30 minutes. You can Align the MACD entry settings equal to your opened timechart or use a slightly higher timeframe. For the MA trend filters, higher timeframe settings such as 30 min, 1 hour, 4 hours, or 1 day are recommended for trading the trend.
Disclaimer
Trading involves significant risk and may not be suitable for all investors. The information provided in this script is for educational purposes only and should not be considered as financial advice. Past performance is not indicative of future results. By using this script, you acknowledge that you understand and accept these risks.
BitBell - EMA PullBack RSI EXO
🔵 Introduction
Version 1.1
This is a Pine 5 trend following strategy. It has a four strategy with several alerts and signals. The design intent is to produce a commercial grade signal generator that can be adapted to any symbol in cryptocurrency and only 1H Chart. Ideally, the script is reliable enough to be the basis of an automated trading system web-hooked to a server with API access to crypto brokerages. The strategy can be run in three different modes: long, short and bidirectional.
As a trend following strategy, the behavior of the script is to buy on strength and sell on weakness. As such the trade orders maintain its directional bias according to price pressure. What you will see on the chart is long positions on the left side of the mountain and short on the right. Long and short positions are not intermingled as long as there exists a detectable trend. This is extremely beneficial feature in long running bull or bear markets. The script uses multiple setups to avoid the situation where you got in on the trend, took a small profit but couldn’t get back in because the logic is waiting for a pullback or some other intricate condition.
Deep draw-downs are a characteristic of trend following systems and this system is no different. However, this script makes use of the TradingView pyramid feature with three NPUs to find better place and even you can change drop percentage in settings for another trigger, accessible from the properties tab.
When trend market break it will stop the trade and usually it takes 2-4 percent loss but don't worry it has prefect money management and you can use it for Futures market and even Spot market.
🔵 Design
This script uses twelve indicators on two time frames. The chart (primary) interval and one higher time frame which is based on the primary. The higher time frame identifies the trend for which the primary will trade. I’ve tried to keep the higher time frame around five times greater than the primary. The original trading algorithms are a port from a much larger program on another trading platform. I’ve converted some of the statistical functions to use standard indicators available on TradingView. The setups make heavy use of the Hull Moving Average in conjunction with EMAs that form the Bill Williams Alligator as described in his book “New Trading Dimensions” Chapter 3. Lag between the Hull and the EMAs form the basis of the entry and exit points. The alligator itself is used to identify the trend main body.
The entire script is around 740 lines of Pine code which is the maximum incidental size given the TradingView limits: local scopes, run-time duration and compile time. I’ve been working on this script for over a year and have tested it on various instruments stock crypto. It performs well on higher liquidity markets that have at least a year of historical data. Though it can be configured to work on any interval between 15 minutes and 4 hour, trend trading is generally a longer term paradigm. For day trading the 10 to 15 minute interval will allow you to catch momentum breakouts. For intraweek trades 30 minutes to 1 hour should give you a trade every other a day.
Inputs to the script use cone centric measurements in effort to avoid exposing adjustments to the various internal indicators. The goal was to keep the inputs relevant to the actual trade entry and exit locations as opposed to a series of MA input values and the like. As a result the strategy exposes over 12 inputs grouped into long or short sections. Inputs are available for the usual minimum profit and stop-loss as well as trade, modes, presets, reports and lots of calibrations. The inputs are numerous, I’m aware. Unfortunately, at this time, TradingView does not offer any other method to get data in the script. The usual initialization files such as cnf, cfg, ini, json and xml files are currently unsupported.
Example configurations for various instruments along with a detailed PDF user manual is available.
it has no repaint i guaranty this, and you can have 10 days free with comment and check it by yourself
One issue that comes up when comparing the strategy with the study is that the strategy trades show on the chart one bar later than the study. This problem is due to the fact that “strategy.entry()” and “strategy_close()” do not execute on the same bar called. The study, on the other hand, has no such limitation since there are no position routines. However, alerts that are subsequently fired off when triggered in the study are dispatched from the TradingView servers one bar later from the study plot. Therefore the alert you actually receive on your cell phone matches the strategy plot but is one bar later than the study plot.
Please be aware that the data source matters. Cryptocurrency has no central tick repository so each exchange supplies TradingView its feed. Even though it is the same symbol the quality of the data and subsequently the bars that are supplied to the chart varies with the exchange. This script will absolutely produce different results on different data feeds of the same symbol. Be sure to backtest this script on the same data you intend to receive alerts for. Any example settings I share with you will always have the exchange name used to generate the test results.
🟡 Usage
It sends long and short signals with pyramid orders of up to 3, meaning that the strategy can trigger up to 3 orders in the same direction. Good risk and money management.
It's important to note that the strategy combines 2 systems working together (Long and LongX). Let’s describe the specific features of this strategy.
🔵 If Findes Supports And Ressitances And Trend Lines As Best As It Can, And You Can See:
🟢 Frist Simple Long Condition = It Look At The Trend Wait For RSI Cross 30 Number Then Ckeck Risk To Reward, check something else such as divergence:
🟢 Another Long Example:
🔴 Frist Simple Short Condition = It Look At The Trend Wait For RSI Cross 70 Number Then Ckeck Risk To Reward, check something else such as divergence:
🔴 Another Short Example:
The following steps provide a very brief set of instructions that will get you started but will most certainly not produce the best backtest. A trading system that you are willing to risk your hard earned capital will require a well crafted configuration that involves time, expertise and clearly defined goals. As previously mentioned, I have several example configs that I use for my own trading that I can share with you along with a PDF which describes each input in detail. To get hands on experience in setting up your own symbol from scratch please follow the steps below.
The input dialog box contains over 12 inputs, There are four options must to be configured: Choose Target, side, Choose Settings, Money Management,and settings that apply to both. The following steps address these four main options only.
Money Management System For Leverage 10:
Bot Finds Last Lower Low And Calculate Distance From Entry Price, Then Cross It To Initial Capitan And Cross Leverage =>
Position_Size = (((1.64) * (initial Capital)) * (leverage))
And Check Dominances Too For Getting Best Money Management Result
🔵 Settings
* Side, You Can Set Long Or Short Or Both.
* Choose Target, You Can Set One Target Or All Targets.
* Money Management, You Can ON Or OFF It, With OFF You Can USE It For SPOT Trades.
* Choose Settings, In This Field You Can Set Mathematical Optimization, Ddepends On Which Pair You USE.
* Clear With Daily PullBack?, With This Check Box You Can Clear Signals With Daily PullBack.
* Long X, You Can Set Long Leverage.
* Short X, You Can Set Short Leverage.
* Second Order X, You Can Set Pyramiding Leverage.
* Target Long, You Can Set Percent For Long Target.
* Target Short, You Can Set Percent For Short Target.
* Short Martin Percent, You Can Set Short Martingale Percent.
* Long Martin Percent, You Can Set Long Martingale Percent.
🟡 Pyraming 3
🟡 Commission Is 0.065 %
🟡 Slippage Is 10 ticks
🔴Only Use For 1 Hour Chart
🔴 CONCLUSION
We believe that success lies in the association of the user with the indicator, opposed to many traders who have the perspective that the indicator itself can make them become profitable. The reality is much more complicated than that.
The aim is to provide an indicator comprehensive, customizable, and intuitive enough that any trader can be led to understand this truth and develop an actionable perspective of technical indicators as support tools for decision making.
🔴 RISK DISCLAIMER
Trading is risky & most day traders lose money. All content, tools, scripts, articles, & education provided by BitBell are purely for informational & educational purposes only. Past performance does not guarantee future results.
IchiBot - [SigmaStreet]
The IchiBot Indicator has been used to develop automated trading systems. It leverages the open-source Ichimoku framework provided by Trading View, to enable users to creatively generate over 1 trillion different combinations of trading conditions with the use of multiple timeframes to create unique “signal labels” that can be used to create custom strategies or provide in depth market analysis. At the end of this description, I have provided an example of input settings for a simple scalping strategy that I have back tested on US30 on the 5 minute timeframe.
Overview of the Settings:
The visuals section includes an option to show or hide certain parts of the indicator and change the size of the signal labels plotted on the chart.
Next to the “Signal color on baseline/candles” section, you can choose if you want to see additional signals generations from the most previous plotted label on a color changing baseline, or color changing candles. A color change from gray to blue/red indicate that the conditions from the most previously plotted signal label have been met again.
The next 5 sections are all related to the strategy portion of the indicator, used to aid in the back testing process. These sections are titled “Stop loss”, “Take Profit”, “Trail Stop”, “Trade Settings” and “Trade Schedule”.
The Stop Loss section includes an option to choose between value of “pts”, “atr” (average true range) or “None”. The stop loss value in “pts” is simply a specified number of points or pips from the current entry price of a trade that are input in the “SL” section. If the stop loss type is “atr” the “SL” section is not used and the value is calculated and displaced from the current entry price of a trade based on the atr period multiplied by the atr multiplier.
The take profit section is based on the same logic as the stop loss.
The Trail Stop section includes an option to choose between values “pts” or “None”. If the Trail Stop value is “pts”, a trailing stop loss is activated if a trade moves a point value into profit that exceeds the value of the “Trail Activation”. If the Trail Offset type is “pts”, the trailing stop loss is placed a point value away from the current price that is equal to the “Trail Offset” value.
The trade settings section has two options to either prevent or allow trade reversals and prevent or allow only 1 trade per signal label.
If the “Don’t allow trade reversals” is on, then a currently active trade can not be cancelled by an opposite trade signal. It can only be cancelled by the exit logic selected in the above sections. If the “One trade per signal” is selected, the strategy will only enter a trade if the most recent signal label is different from the last signal label where a trade was entered, or if the most recent signal label is in the opposite direction of the most recent signal label where a trade was entered.
The trade schedule section includes an option to only generate signal labels during the specified time. You can choose between 24/7 which will generate signals without any time restriction, or you can choose a custom time which is based on the America / New York time zone.
The timeframe settings section includes an option to choose “single” or “multiple” timeframes, as well as an option to show every signal label combination (“all”), or only the signal labels with the highest numerical value (“absolute”).
If you select “single” next to “timeframe”, the indicator will show you labels based on trade conditions met from only 1 selected timeframe. If you select “multiple” next to “timeframe”, the indicator is designed to return signal labels based on trade conditions that have been met on at least 2 different timeframes.
If you select “multiple” and “use current timeframe”, the indicator will include labels that always include a minimum of 2 timeframes where 1 timeframe is always the current timeframe. If you unselect the “use current timeframe”, the indicator will include labels with a minimum of 2 timeframes.
If you select “multiple” next to “timeframe” and “all” next to “Show all/absolute labels”, the indicator will show you every possible combination of labels that vary from trade conditions met on a minimum of 2 timeframes, to the maximum number of timeframes selected.
If you select “multiple” next to “timeframe” and “absolute” next to “Show all/absolute labels”, the indicator will only show you labels where the numerical value is equivalent to the maximum number of timeframes selected.
Each signal label provides a number which refers to the number of timeframes used to generate the label, offering insights briefly. Hover over a label to reveal detailed tooltip information that details the exact timeframes used to generate each label.
You can choose all from “Show all/absolute labels” to see every possible combination of trade signals or “absolute” to only see labels that have the highest possible numerical value. Absolute means that every condition selected from every timeframe was calculated to be true at the same time on the same candle.
The next 8 sections are “Current timeframe trade conditions”, “1-minute timeframe trade conditions”, “5-minute timeframe trade conditions”, “15-minute timeframe trade conditions”, “30-minute timeframe trade conditions”, “1-hour timeframe trade conditions”, “4-hour timeframe trade conditions”, “Daily timeframe trade conditions”.
These sections include the same 10 trade conditions, that can be used independently, or in combination with each other. This brings the total number of trade conditions to 70.
The final section includes a standard option to adjust the current Ichimoku values.
Understanding the Calculations:
The term “future” refers to a value that is calculated 26 candles to the right of the most recent closing price.
The term “current” refers to a value that is calculated on the most recent closing price.
The term “past” refers to a value that is calculated 26 candles to the left of the most recent closing price.
Bullish is referred to as “blue” and bearish is referred to as “red”.
Buy Signals:
1. The current closing price is greater than the current cloud value.
2. The future cloud is blue.
3. The current closing price is greater than the current conversion line.
4. The current conversion line is greater than the current baseline.
5. The lagging span is greater than the closing price of the last 25 candles.
6. The lagging span is greater than the past cloud.
7. The lagging span is greater than the past conversion line and the past baseline.
8. The current conversion line is greater than the current cloud.
9. The current baseline is greater than the current cloud.
10. The value of the current cloud to the future cloud is completely blue.
Sell Signals:
1. The current closing price is less than the current cloud value.
2. The future cloud is red.
3. The current closing price is less than the current conversion line.
4. The current conversion line is less than the current baseline.
5. The lagging span is less than the closing price of the last 25 candles.
6. The lagging span is less than the past cloud.
7. The lagging span is less than the past conversion line and the past baseline.
8. The current conversion line is less than the current cloud.
9. The current baseline is less than the current cloud.
10. The value of the current cloud to the future cloud is completely red.
The script enables users to access the value of these 10 trade conditions across the 7 major time frames (1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 4-hour, Daily, and the current charts time frame) by using the official non repainting request security function provided by Trading View:
f_secSecurity(_src, _res, _exp) =>
request.security(_src, _res, _exp )
This indicator provides up to 70 variables (10 variables X 7 timeframes) that can be used separately, or in combination to generate signal labels.
Enhance your visual analysis with a color-changing baseline and candle colors that adapt to signal shifts, offering an immediate understanding of market trends. The base line will change from gray to blue/red which will reference the most previously plotted signal label. This change in color indicate that the conditions from the most recently plotted signal label have been met once again. Please refer to the example below.
Adjustments to the Ichimoku Indicator:
The script uses a slightly refined version of the Ichimoku indicator to calculate 10 different “trade conditions”. Each trade condition can create 1 bullish signal label and 1 bearish signal label. The calculations are primarily based on “greater than and less than logic” which is standard for signal generation.
In the original Ichimoku calculations, the “Lagging Span” has a default value of 26 periods. In the actual calculations, this input with the title “Lagging Span” is referred to as the “displacement”. When the lagging span is plotted on the chart, it is plotted with an offset value of offset = -displacement + 1 which technically plots the lagging span 25 candles to the left the most recent candle (if you count the most recent closing price as 0 and not 1). The clouds are plotted with an offset of offset = displacement -1 which technically plots the clouds 25 candles to the right of the most recent candle.
I have adjusted the logic of the Ichimoku indicator so the lagging span is still plotted 25 candles to the left of the most recently confirmed candle close, but the cloud is plotted 26 candles to the right of the most recent confirmed candle close.
This seemingly small adjustment of one candle cannot simply be adjusted in the settings of the original Ichimoku indicator since the calculations of the cloud and lagging span displacements are directly affected by the same value (displacement = 26, also known as the “lagging span”). My script is adjusted to make calculations where the lagging span is 25 candles to the left of the most recent candle, and the cloud is displaced 26 candles to the right of the most recent candle.
For example, my scripts logic to detect if the current closing price is over the current cloud is (close > leadLead1 and close > leadLine2 and leadLine1 > leadLine2 . By using a lookback of , the logic assumes that the displaced value is 26 bars to the right of the most recent candle. My script also reflects this logic in the plotted values of the cloud where the offset values are offset = displacement. This adjustment is made without affecting any other part of the Ichimoku indicators calculations, only the displacement of the cloud which directly affects the logic of trade conditioins. This change is a deliberate and necessary function of this script’s logic to generate trade conditions and signal labels.
I’ve removed the conversion line and the lagging span and introduced a 26-period pivot high/low to provide a less cluttered chart. The pivot high/low looks 26 periods to the left and only 1 period to the right. The lagging span and conversion line logic is still built into the framework of the trading signals. If you choose to enable the lagging span, or conversion line.
trading approach, and always test your strategies thoroughly.
The function to generate the "Signal Labels" calculates every single possible combination of the 7 different timeframes which is a total of 127 combinations for bullish signal labels, and 127 combinations for bearish signal labels. This function also provides the necessary criteria for the strategy entry conditions, based on the dynamically calculated values derived from the signal labels themselves. For example: "buy signal on 1 minute and 5 minute timeframe" is considered 1 combination, and "Buy signal on current, 5 minute, 15 minute, 30 minute, 1 hour, 4 hour and daily timeframe" is also considered 1 combination. There are a total of 254 combinations between buy and sell signal labels along with 254 individual variables with their own unique tool tip description. The signal label function alone spans over 1340 lines of code (minus spaces and comments) to specifically account for every possible variable combination. This unique and original function also calculates the signal label "value" which is the number you see on the signal label. This function adjusts the amount of labels plotted, the value and description of all labels based on the timeframe settings "single"/"multiple", the use of "use current timeframe" setting, and the "trade schedule". This signal label function has been a landmark piece of code for me in my endeavor to create and optimize my strategies based on its ability to provide an in depth analysis of the timeframes used when generating signal labels. This function is main reason that this script has been published closed source.
Back tested results.
The current results are from US30 (Dow Jones Industrial Average CFD) on the 5-minute timeframe using regular candles. The inputs are as follows:
Stop loss = 5000 pts
No take profit.
Trail activation = 100 pts
Trail offset = 100 pts
Don’t allow trade reversals
Trade 24/7
Timeframe = multiple
Show absolute signals
Use current timeframe, lag span over/under candles
Use 30m timeframe, all cloud is bull/bear
Initial capital = $10,000 USD, 1 contract, $0.07 per contract, slippage = 3 ticks, use bar magnifier = on
Timeframe = June 1st, 2023 – November 10th, 2023, risk = 5% (greatest loosing trade = $500.44)
Heatmap MACD Strategy - Pineconnector (Dynamic Alerts)Hello traders
This script is an upgrade of this template script.
Heatmap MACD Strategy
Pineconnector
Pineconnector is a trading bot software that forwards TradingView alerts to your Metatrader 4/5 for automating trading.
Many traders don't know how to dynamically create Pineconnector-compatible alerts using the data from their TradingView scripts.
Traders using trading bots want their alerts to reflect the stop-loss/take-profit/trailing-stop/stop-loss to breakeven options from your script and then create the orders accordingly.
This script showcases how to create Pineconnector alerts dynamically.
Pineconnector doesn't support alerts with multiple Take Profits.
As a workaround, for 2 TPs, I had to open two trades.
It's not optimal, as we end up paying more spreads for that extra trade - however, depending on your trading strategy, it may not be a big deal.
TradingView Alerts
1) You'll have to create one alert per asset X timeframe = 1 chart.
Example : 1 alert for EUR/USD on the 5 minutes chart, 1 alert for EUR/USD on the 15-minute chart (assuming you want your bot to trade the EUR/USD on the 5 and 15-minute timeframes)
2) For each alert, the alert message is pre-configured with the text below
{{strategy.order.alert_message}}
Please leave it as it is.
It's a TradingView native variable that will fetch the alert text messages built by the script.
3) Don't forget to set the webhook URL in the Notifications tab of the TradingView alerts UI.
EA configuration
The Pyramiding in the EA on Metatrader must be set to 2 if you want to trade with 2 TPs => as it's opening 2 trades.
If you only want 1 TP, set the EA Pyramiding to 1.
Regarding the other EA settings, please refer to the Pineconnector documentation on their website.
Logger
The Pineconnector commands are logged in the TradingView logger.
You'll find more information about it from this TradingView blog post
Important Notes
1) This multiple MACDs strategy doesn't matter much.
I could have selected any other indicator or concept for this script post.
I wanted to share an example of how you can quickly upgrade your strategy, making it compatible with Pineconnector.
2) The backtest results aren't relevant for this educational script publication.
I used realistic backtesting data but didn't look too much into optimizing the results, as this isn't the point of why I'm publishing this script.
3) This template is made to take 1 trade per direction at any given time.
Pyramiding is set to 1 on TradingView.
The strategy default settings are:
Initial Capital: 100000 USD
Position Size: 1 contract
Commission Percent: 0.075%
Slippage: 1 tick
No margin/leverage used
For example, those are realistic settings for trading CFD indices with low timeframes but not the best possible settings for all assets/timeframes.
Concept
The Heatmap MACD Strategy allows selecting one MACD in five different timeframes.
You'll get an exit signal whenever one of the 5 MACDs changes direction.
Then, the strategy re-enters whenever all the MACDs are in the same direction again.
It takes:
long trades when all the 5 MACD histograms are bullish
short trades when all the 5 MACD histograms are bearish
You can select the same timeframe multiple times if you don't need five timeframes.
For example, if you only need the 30min, the 1H, and 2H, you can set your timeframes as follow:
30m
30m
30m
1H
2H
Risk Management Features
All the features below are pips-based.
Stop-Loss
Trailing Stop-Loss
Stop-Loss to Breakeven after a certain amount of pips has been reached
Take Profit 1st level and closing X% of the trade
Take Profit 2nd level and close the remaining of the trade
Custom Exit
I added the option ON/OFF to close the opened trade whenever one of the MACD diverges with the others.
Help me help the community
If you see any issue when adding your strategy logic to that template regarding the orders fills on your Metatrader, please let me know in the comments.
I'll use your feedback to make this template more robust. :)
What's next?
I'll publish a more generic template built as a connector so you can connect any indicator to that Pineconnector template.
Then, I'll publish a template for Capitalise AI, ProfitView, AutoView, and Alertatron.
Thank you
Dave
Heatmap MACD StrategyHello traders
A customer gave me the idea indirectly after I made an update to that script:
Supertrend MTF Heatmap
Important Notes
The backtest results aren't relevant for this educational script publication.
I used realistic backtesting data but didn't look too much into optimizing the results, as this isn't the point of why I'm publishing this script.
I wanted to showcase that any Heatmap script can be converted into a strategy.
The strategy default settings are:
Initial Capital: 100000 USD
Position Size: 1 contract
Commission Percent: 0.075%
Slippage: 1 tick
No margin/leverage used
For example, those are realistic settings for trading CFD indices with low timeframes, but not the best possible settings for all assets/timeframes.
Concept
The Heatmap MACD Strategy allows selecting one MACD in five different timeframes.
You'll get an exit signal whenever one of the 5 MACDs changes direction.
Then, the strategy re-enters whenever all the MACDs are in the same direction again.
It takes:
long trades when all the 5 MACD histograms are bullish
short trades when all the 5 MACD histograms are bearish
You can select the same timeframe multiple times if you don't need five timeframes.
For example, if you only need the 30min, the 1H, and 2H, you can set your timeframes as follow:
30m
30m
30m
1H
2H
Risk Management Features
Nothing too fancy
All the features below are pips-based
Stop-Loss
Trailing Stop-Loss
Stop-Loss to Breakeven after a certain amount of pips has been reached
Take Profit 1st level and closing X% of the trade
Take Profit 2nd level and close the remaining of the trade
What's next?
I'll publish this script's open-source Pineconnector, ProfitView, and AutoView versions for educational purposes.
Thank you
Dave
Risk Management and Positionsize - MACD exampleMastering Risk Management
Risk management is the cornerstone of successful trading, and it's often the difference between turning a profit and suffering a loss. In light of its importance, I share a risk management tool which you can use for your trading strategies. The script not only assists in position sizing but also comes with built-in technical features that help in market timing. Let's delve into the nitty-gritty details.
Input Parameter: MarginFactor
One of the key features of the script is the MarginFactor input parameter. This element lets you control the portion of your equity used for placing each trade. A MarginFactor of -0.5 means 50% of your total equity will be deployed in placing the position size. Although Tradingview has a built-in option to adjust position sizing in a same way, I personally prefer to have the logic in my pinecode script. The main reason is userexperience in managing and testing different settings for different charts, timeframes and instruments (with the same strategy).
Stoploss and MarginFactor
If your strategy has a 4% stop-loss, you can choose to use only 50% of your equity by setting the MarginFactor to -0.5. In this case, you are effectively risking only 2% of your total capital per trade, which aligns well with the widely-accepted rule of thumb suggesting a 1-2% risk per trade. Similar if your stoploss is only 1% you can choose to change the MarginFactor to 1, resulting in a positionsize of 200% of your equity. The total risk would be again 2% per trade if your stoploss is set to 1%.
Max Drawdown and MarginFactor
Your MarginFactor setting can also be aligned with the maximum drawdown of your strategy, seen during a backtested period of 2-3 years. For example, if the max drawdown is 15%, you could calibrate your MarginFactor accordingly to limit your risk exposure.
Option to Toggle Number of Contracts
The script offers the option to toggle between using a percentage of equity for position sizing or specifying a fixed number of contracts. Utilizing a percentage of equity might yield unrealistic backtest results, especially over longer periods. This occurs because as the capital grows, the absolute position size also increases, potentially inflating the accumulated returns generated by the backtester. On the other hand, setting a fixed number of contracts as your position size offers a more stable and realistic ROI over the backtested period, as it removes the compounding effect on position sizes.
Key Features Strategy
MACD High Time Frame Entry and Exit Logic
The strategy employs a high time frame MACD (Moving Average Convergence Divergence) to make entry and exit decisions. You can easily adjust the timeframe settings and MACD settings in the inputsection to trade on lower timeframes. For more information on the HTF MACD with dynamic smoothing see:
Moving Average High Time Frame Filter
To reduce market 'noise', the strategy incorporates a high time frame moving average filter. This ensures that the trades are aligned with the dominant market trend (trading the trend). In the inputsection traders can easily switch between different type of moving averages. For more information about this HTF filter see:
Dynamic Smoothing
The script includes a feature for dynamic smoothing. The script contains The timeframeToMinutes(tf) function to convert any given time frame into its equivalent in minutes. For example, a daily (D) time frame is converted into 1440 minutes, a weekly (W) into 10,080 minutes, and so forth. Next the smoothing factor is calculated by dividing the minutes of the higher time frame by those of the current time frame. Finally, the script applies a Simple Moving Average (SMA) over the MACD, SIGNAL, and HIST values, MA filter using the dynamically calculated smoothing factor.
User Convenience: One of the major benefits is that traders don't need to manually adjust the smoothing factor when switching between different time frames. The script does this dynamically.
Visual Consistency: Dynamic smoothing helps traders to more accurately visualize and interpret HTF indicators when trading on lower time frames.
Time Frame Restriction: It's crucial to note that the operational time frame should always be lower than the time frame selected in the input sections for dynamic smoothing to function as intended.
By incorporating this dynamic smoothing logic, the script offers traders a nuanced yet straightforward way to adapt High Time Frame indicators for lower time frame trading, enhancing both adaptability and user experience.
Limitations: Exit Strategy
It's crucial to note that the script comes with a simplified exit strategy, devoid of features like a stop-loss, trailing stop-loss or multiple take profits. This means that while the script focuses on entries and risk management, it might result in higher losses if market conditions unexpectedly turn unfavorable.
Conclusion
Effective risk management is pivotal for trading success, and this TradingView script is designed to give you a better idea how to implement positions sizing with your preferred strategy. However, it's essential to note that this tool should not be considered financial advice. Always perform your due diligence and consult with financial advisors before making any trading decisions.
Feel free to use this risk management tool as building block in your trading scripts, Happy Trading!
TASC 2023.09 The Weekly Factor█ OVERVIEW
TASC's September 2023 edition of Traders' Tips features an article written by Andrea Unger titled “The Weekly Factor", discussing the application of price patterns as filters for trade entries. This script implements a sample trading strategy presented in the article for demonstration purposes only. It explores how the strategy's equity curve might benefit from filtering trade entries using a specific price pattern.
█ CONCEPTS
Pattern filters represent valuable tools that assess current market conditions based on price movements and determine when those conditions become more favorable for trade entries.
The filter used and tested in this article is a metric called the "weekly factor", which measures the price range over the last five trading days and compares it to the open of the session five days ago and the close of the session one day ago (i.e., the "body" of the five-day period). When the five-day body is small compared to the five-day range, this could indicate "indecision" or "compression", potentially followed by a price expansion. Thus, the weekly factor metric can help identify areas in the market where a period of compression might signal a potential breakout.
This script demonstrates the use of the weekly factor for a sample intraday trading strategy (intended for educational and exploratory purposes only). In this strategy, the entry signal is triggered when a 15-minute bar breaks out of the previous day's high-low range, and the position is closed at the end of the day.
█ CALCULATIONS
The script uses two timeframes:
• The strategy entries are processed on the 15-minute timeframe.
• The weekly factor is obtained from the daily timeframe using the request.security function and the following formula:
math.abs(open - close ) < RangeFilter * (ta.highest(5) - ta.lowest(5) )
Here, RangeFilter is an input that can be optimized to find the favorable ratio between the five-day body and the five-day range. Smaller RangeFilter values will lead to fewer trade entries. A RangeFilter value of 1 is equivalent to turning off the filtering altogether.
