Sortino RatioThe Sortino ratio is a variation of the Sharpe ratio that differentiates harmful volatility from total overall volatility by using the asset's standard deviation of negative portfolio returns—downside deviation—instead of the total standard deviation of portfolio returns. The Sortino ratio takes an asset or portfolio's return and subtracts the risk-free rate, and then divides that amount by the asset's downside deviation. The ratio was named after Frank A. Sortino.
Statistics
Normalized Performance ComparisonThis script visualizes the relative performance of a primary asset against a benchmark composed of three reference assets. Here's how it works:
User Inputs:
- Users specify ticker symbols for three reference assets (default: Platinum, Palladium, Rhodium).
Data Retrieval:
- Fetches closing prices for the primary asset (the one the script is applied to) and the three reference assets.
Normalization:
- Each asset's price is normalized by dividing its current price by its initial price at the start of the chart. This allows for performance comparison on a common scale.
Benchmark Creation:
- The normalized prices of the three reference assets are combined to create a composite benchmark.
Ratio Calculation:
- Computes the ratio of the normalized primary asset price to the combined normalized benchmark price, highlighting relative performance.
Plotting:
- Plots this ratio as a blue line on the chart, showing the primary asset's performance relative to the benchmark over time.
This script helps users quickly assess how well the primary asset is performing compared to a set of reference assets.
RTH/ETH Session RangesSimple script that adds a table to the bottom left of the chart - shows the high and low of the Full Session with range, and shows the high and low of the RTH/USA session with same calculations.
This simple script enhances your charting experience by adding a comprehensive table to the bottom left corner of your trading chart. The table is designed to provide key market data at a glance, specifically focusing on the high and low metrics for different trading sessions. Here's a breakdown of what the script offers:
Features of the Script
Full Session Data:
High: The highest price point reached during the entire trading session.
Low: The lowest price point reached during the entire trading session.
Range: The difference between the high and low prices, providing insight into the session's volatility.
RTH/USA Session Data (Regular Trading Hours):
High: The highest price point reached during the RTH, typically reflecting the most active part of the trading day.
Low: The lowest price point reached during the RTH.
Range: The difference between the high and low prices during the RTH, indicating the session's intraday volatility.
How to Use the Script for Trading
Identify Key Levels:
Use the high and low points to identify significant support and resistance levels. These levels can guide your entry and exit points, helping you make informed trading decisions.
Gauge Market Volatility:
The range values for both the Full Session and RTH provide a quick snapshot of market volatility. Higher ranges suggest more significant price movements, which can inform your risk management strategies and position sizing.
Compare Sessions:
By comparing the Full Session data with the RTH data, you can identify differences in price behavior between the broader market hours and the more active trading periods. This comparison can help in understanding market dynamics and planning trades accordingly.
Unique Aspects of the Script
Ease of Access: The table's placement in the bottom left corner ensures that it is always visible without obstructing the main chart view, allowing for quick reference without disrupting your analysis.
Comprehensive Insights: By covering both the Full Session and RTH, the script provides a holistic view of the market, catering to traders who focus on different timeframes.
Customization Potential: Although simple, the script can be customized further to include additional metrics or visual tweaks to better suit individual trading strategies.
Practical Example
Imagine you're trading a particular stock and want to decide on a potential breakout strategy. By using this script, you can quickly identify the high of the Full Session as a potential breakout point. If the price approaches this level during the RTH, you can prepare to enter a trade with the confidence that this level has previously acted as a significant resistance. Conversely, knowing the low of the RTH can help you set stop-loss orders to manage risk effectively.
Random Entry and ExitStrategy for Researching Whether It Is Possible to Earn Consistently by Opening Random Trades
The essence of the strategy lies in generating random entries and exits based on pseudorandom numbers. The generation of pseudorandom numbers is performed by the function random_number based on the value of the seed variable. The variables entry_threshold and exit_threshold control the frequency of entries and exits. Lower values mean less frequent trades. To increase the number of trades, increase the values of these variables.
The strategy was created as part of research into whether it is possible to earn randomly in financial markets by making chaotic actions of opening and closing trades. However, it adheres to a few rules: open only long positions (in the direction of the global trend) and do not use leverage. Positions are opened with the entire available capital.
100 generations of the strategy on the daily chart of the S&P 500 (seed 1-101) give 100% positive mathematical expectations. Similar results are observed on higher timeframes of assets that are in a global uptrend.
There is also the possibility of opening only short positions for the research. Note that the logic of the strategy is built in such a way that only one trading direction can operate simultaneously (either longs or shorts). On higher timeframes, random shorts show negative results. Positive mathematical expectations for short positions can be found on lower timeframes (1 min, etc.), where a large amount of noise is observed.
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Стратегия для исследования, можно ли стабильно зарабатывать при открытии случайных сделок
Суть стратегии заключается в генерации случайных входов и выходов на основе псевдослучайных чисел. Генерация псевдослучайных чисел происходит функцией random_number на основе значения переменной seed. Переменные entry_threshold и exit_threshold контролируют частоту входов и выходов. Более низкие значения означают менее частые сделки. Для увеличения количества сделок - увеличивайте значения переменных.
Стратегия создавалась в рамках исследования вопроса, можно ли случайным образом зарабатывать на фин. рынках, совершая хаотичные открытия и закрытия сделок. НО, придерживаясь нескольких правил: открывать только длинные позиции (в сторону глобального тренда) и не использовать кредитные плечи. Открытие позиций происходит на весь доступный капитал.
100 генераций стратегии на дневном графике S&P500 (seed 1-101) дают 100% положительных математических ожиданий. Похожие результаты наблюдаются на высоких таймфреймах активов, которые глобально находятся в восходящем тренде.
Также для исследования предусмотрена возможность открытия только коротких позиций. Обратите внимание, что логика стратегии построена таким образом, что одновременно может работать только одно направление торговли (либо лонги, либо шорты). На старших таймфреймах случайные шорты показывают негативные результаты. Положительное математическое ожидание для коротких позиций можно обнаружить на младших таймфреймах (1 min, etc), где наблюдается большое количество шумов.
[InvestorUnknown] Performance MetricsOverview
The Performance Metrics indicator is a tool designed to help traders and investors understand and utilize key performance metrics in their strategies. This indicator is inspired by the Rolling Risk-Adjusted Performance Ratios created by @EliCobra, but it offers enhanced usability and additional features to provide a more user-friendly code for understanding the calculations.
Features
Rolling Lookback:
Dynamic Lookback Calculation: The indicator automatically calculates the number of bars from the start of the asset's price history, up to a maximum of 5000 bars due to TradingView platform restrictions.
Adjustable Lookback Period: Users can manually set a lookback period or choose to use the rolling lookback feature for dynamic calculations.
RollingLookback() =>
x = bar_index + 1
y = x > 4999 ? 5000 : x > 1 ? (x - 1) : x
y
Trend Analysis
The Trend Analysis section in this indicator helps traders identify the direction of the market trend based on the balance of positive and negative returns over time. This is achieved by calculating the sums of positive and negative returns and optionally smoothing these values to provide a clearer trend signal.
Configuration: Enable smoothing if you want to reduce noise in the trend analysis. Choose between EMA and SMA for smoothing. Set the length for smoothing according to your preference for sensitivity (shorter lengths are more sensitive to changes, longer lengths provide smoother signals).
Interpretation:
- A positive trend difference (filled with green) indicates a bullish trend, suggesting more positive returns.
- A negative trend difference (filled with red) indicates a bearish trend, suggesting more negative returns.
- Colored bars provide a quick visual cue on the trend direction, helping to make timely trading decisions.
// The Trend Analysis section calculates and optionally smooths the sums of positive and negative returns.
// This helps identify the trend direction based on the balance of positive and negative returns over time.
Ps = Smooth ? Smooth_type == "EMA" ? ta.ema(pos_sum, Smooth_len) : ta.sma(pos_sum, Smooth_len) : pos_sum
Ns = Smooth ? Smooth_type == "EMA" ? ta.ema(neg_sum, Smooth_len) : ta.sma(neg_sum, Smooth_len) : neg_sum
// Calculate the difference between smoothed positive and negative sums
dif = Ps + Ns
Performance Metrics Table
Visual Table Display: Option to display a table on the chart with calculated performance metrics. This table includes comprehensive metrics like Mean Return, Positive and Negative Mean Return, Standard Deviation, Sharpe Ratio, Sortino Ratio, and Omega Ratio.
Performance Metrics Calculated
Mean Return:
Description: The average return over the lookback period.
Purpose: Helps in understanding the overall performance of the asset by providing a simple average of returns.
Positive Mean Return:
Description: The average of all positive returns over the lookback period.
Purpose: Highlights the average gain during profitable periods, giving insight into the asset's potential upside.
Negative Mean Return:
Description: The average of all negative returns over the lookback period.
Purpose: Focuses on the average loss during unprofitable periods, helping to assess the downside risk.
Standard Deviation (STDEV):
Description: A measure of volatility that calculates the dispersion of returns from the mean.
Purpose: Indicates the risk associated with the asset. Higher standard deviation means higher volatility and risk.
Sharpe Ratio:
Description: A risk-adjusted return metric that divides the mean return by the standard deviation of returns. It can be annualized if selected.
Purpose: Provides a standardized way to compare the performance of different assets by considering both return and risk. A higher Sharpe Ratio indicates better risk-adjusted performance.
sharpe_ratio = mean_all / stddev_all * (Annualize ? math.sqrt(Lookback) : 1)
Sortino Ratio:
Description: Similar to the Sharpe Ratio but focuses only on downside volatility. It divides the mean return by the standard deviation of negative returns. It can be annualized if selected.
Purpose: Offers a better assessment of downside risk by ignoring upside volatility. A higher Sortino Ratio indicates a higher return per unit of downside risk.
sortino_ratio = mean_all / stddev_neg * (Annualize ? math.sqrt(Lookback) : 1)
Omega Ratio:
Description: The ratio of the probability-weighted average of positive returns to the probability-weighted average of negative returns.
Purpose: Measures the overall likelihood of positive returns compared to negative returns. An Omega Ratio greater than 1 indicates more frequent and/or larger positive returns compared to negative returns.
omega_ratio = (prob_pos * mean_pos) / (prob_neg * -mean_neg)
By calculating and displaying these metrics, the indicator provides a comprehensive view of the asset's performance, enabling traders and investors to make informed decisions based on both returns and risk-adjusted metrics.
Use Cases:
Performance Evaluation: Assesses an asset's performance by analyzing both returns and risk factors, giving a clear picture of profitability and volatility.
Risk Comparison: Compares the risk-adjusted returns of different assets or portfolios, aiding in identifying investments with superior risk-reward trade-offs.
Risk Management: Helps manage risk exposure by evaluating downside risks and overall volatility, enabling more informed and strategic investment decisions.
Bayesian Trend Indicator [ChartPrime]Bayesian Trend Indicator
Overview:
In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
The "Bayesian Trend Indicator" is a sophisticated technical analysis tool designed to assess the direction of price trends in financial markets. It combines the principles of Bayesian probability theory with moving average analysis to provide traders with a comprehensive understanding of market sentiment and potential trend reversals.
At its core, the indicator utilizes multiple moving averages, including the Exponential Moving Average (EMA), Simple Moving Average (SMA), Double Exponential Moving Average (DEMA), and Volume Weighted Moving Average (VWMA) . These moving averages are calculated based on user-defined parameters such as length and gap length, allowing traders to customize the indicator to suit their trading strategies and preferences.
The indicator begins by calculating the trend for both fast and slow moving averages using a Smoothed Gradient Signal Function. This function assigns a numerical value to each data point based on its relationship with historical data, indicating the strength and direction of the trend.
// Smoothed Gradient Signal Function
sig(float src, gap)=>
ta.ema(source >= src ? 1 :
source >= src ? 0.9 :
source >= src ? 0.8 :
source >= src ? 0.7 :
source >= src ? 0.6 :
source >= src ? 0.5 :
source >= src ? 0.4 :
source >= src ? 0.3 :
source >= src ? 0.2 :
source >= src ? 0.1 :
0, 4)
Next, the indicator calculates prior probabilities using the trend information from the slow moving averages and likelihood probabilities using the trend information from the fast moving averages . These probabilities represent the likelihood of an uptrend or downtrend based on historical data.
// Define prior probabilities using moving averages
prior_up = (ema_trend + sma_trend + dema_trend + vwma_trend) / 4
prior_down = 1 - prior_up
// Define likelihoods using faster moving averages
likelihood_up = (ema_trend_fast + sma_trend_fast + dema_trend_fast + vwma_trend_fast) / 4
likelihood_down = 1 - likelihood_up
Using Bayes' theorem , the indicator then combines the prior and likelihood probabilities to calculate posterior probabilities, which reflect the updated probability of an uptrend or downtrend given the current market conditions. These posterior probabilities serve as a key signal for traders, informing them about the prevailing market sentiment and potential trend reversals.
// Calculate posterior probabilities using Bayes' theorem
posterior_up = prior_up * likelihood_up
/
(prior_up * likelihood_up + prior_down * likelihood_down)
Key Features:
◆ The trend direction:
To visually represent the trend direction , the indicator colors the bars on the chart based on the posterior probabilities. Bars are colored green to indicate an uptrend when the posterior probability is greater than 0.5 (>50%), while bars are colored red to indicate a downtrend when the posterior probability is less than 0.5 (<50%).
◆ Dashboard on the chart
Additionally, the indicator displays a dashboard on the chart , providing traders with detailed information about the probability of an uptrend , as well as the trends for each type of moving average. This dashboard serves as a valuable reference for traders to monitor trend strength and make informed trading decisions.
◆ Probability labels and signals:
Furthermore, the indicator includes probability labels and signals , which are displayed near the corresponding bars on the chart. These labels indicate the posterior probability of a trend, while small diamonds above or below bars indicate crossover or crossunder events when the posterior probability crosses the 0.5 threshold (50%).
The posterior probability of a trend
Crossover or Crossunder events
◆ User Inputs
Source:
Description: Defines the price source for the indicator's calculations. Users can select between different price values like close, open, high, low, etc.
MA's Length:
Description: Sets the length for the moving averages used in the trend calculations. A larger length will smooth out the moving averages, making the indicator less sensitive to short-term fluctuations.
Gap Length Between Fast and Slow MA's:
Description: Determines the difference in lengths between the slow and fast moving averages. A higher gap length will increase the difference, potentially identifying stronger trend signals.
Gap Signals:
Description: Defines the gap used for the smoothed gradient signal function. This parameter affects the sensitivity of the trend signals by setting the number of bars used in the signal calculations.
In summary, the "Bayesian Trend Indicator" is a powerful tool that leverages Bayesian probability theory and moving average analysis to help traders identify trend direction, assess market sentiment, and make informed trading decisions in various financial markets.
Bitcoin Futures vs. Spot Tri-Frame - Strategy [presentTrading]Prove idea with a backtest is always true for trading.
I developed and open-sourced it as an educational material for crypto traders to understand that the futures and spot spread may be effective but not be as effective as they might think. It serves as an indicator of sentiment rather than a reliable predictor of market trends over certain periods. It is better suited for specific trading environments, which require further research.
█ Introduction and How it is Different
The "Bitcoin Futures vs. Spot Tri-Frame Strategy" utilizes three different timeframes to calculate the Z-Score of the spread between BTC futures and spot prices on Binance and OKX exchanges. The strategy executes long or short trades based on composite Z-Score conditions across the three timeframes.
The spread refers to the difference in price between BTC futures and BTC spot prices, calculated by taking a weighted average of futures prices from multiple exchanges (Binance and OKX) and subtracting a weighted average of spot prices from the same exchanges.
BTCUSD 1D L/S Performance
█ Strategy, How It Works: Detailed Explanation
🔶 Calculation of the Spread
The spread is the difference in price between BTC futures and BTC spot prices. The strategy calculates the spread by taking a weighted average of futures prices from multiple exchanges (Binance and OKX) and subtracting a weighted average of spot prices from the same exchanges. This spread serves as the primary metric for identifying trading opportunities.
Spread = Weighted Average Futures Price - Weighted Average Spot Price
🔶 Z-Score Calculation
The Z-Score measures how many standard deviations the current spread is from its historical mean. This is calculated for each timeframe as follows:
Spread Mean_tf = SMA(Spread_tf, longTermSMA)
Spread StdDev_tf = STDEV(Spread_tf, longTermSMA)
Z-Score_tf = (Spread_tf - Spread Mean_tf) / Spread StdDev_tf
Local performance
🔶 Composite Entry Conditions
The strategy triggers long and short entries based on composite Z-Score conditions across all three timeframes:
- Long Condition: All three Z-Scores must be greater than the long entry threshold.
Long Condition = (Z-Score_tf1 > zScoreLongEntryThreshold) and (Z-Score_tf2 > zScoreLongEntryThreshold) and (Z-Score_tf3 > zScoreLongEntryThreshold)
- Short Condition: All three Z-Scores must be less than the short entry threshold.
Short Condition = (Z-Score_tf1 < zScoreShortEntryThreshold) and (Z-Score_tf2 < zScoreShortEntryThreshold) and (Z-Score_tf3 < zScoreShortEntryThreshold)
█ Trade Direction
The strategy allows the user to specify the trading direction:
- Long: Only long trades are executed.
- Short: Only short trades are executed.
- Both: Both long and short trades are executed based on the Z-Score conditions.
█ Usage
The strategy can be applied to BTC or Crypto trading on major exchanges like Binance and OKX. By leveraging discrepancies between futures and spot prices, traders can exploit market inefficiencies. This strategy is suitable for traders who prefer a statistical approach and want to diversify their timeframes to validate signals.
█ Default Settings
- Input TF 1 (60 minutes): Sets the first timeframe for Z-Score calculation.
- Input TF 2 (120 minutes): Sets the second timeframe for Z-Score calculation.
- Input TF 3 (180 minutes): Sets the third timeframe for Z-Score calculation.
- Long Entry Z-Score Threshold (3): Defines the threshold above which a long trade is triggered.
- Short Entry Z-Score Threshold (-3): Defines the threshold below which a short trade is triggered.
- Long-Term SMA Period (100): The period used to calculate the simple moving average for the spread.
- Use Hold Days (true): Enables holding trades for a specified number of days.
- Hold Days (5): Number of days to hold the trade before exiting.
- TPSL Condition (None): Defines the conditions for taking profit and stop loss.
- Take Profit (%) (30.0): The percentage at which the trade will take profit.
- Stop Loss (%) (20.0): The percentage at which the trade will stop loss.
By fine-tuning these settings, traders can optimize the strategy to suit their risk tolerance and trading style, enhancing overall performance.
PE BandThe PE Band shows the highest and lowest P/E in the previous period with TTM EPS. If the current P/E is lower than the minimum P/E, it is considered cheap. In other words, higher than the maximum P/E is considered expensive.
PE Band consists of 2 lines.
- Firstly, the historical P/E value in "green" (if TTM EPS is positive) or "red" (if TTM EPS is negative) states will change according to the latest high or low price of TTM EPS, such as: :
After the second quarter of 2023 (end of June), how do prices from 1 July – 30 September reflect net profits? The program will get the highest and lowest prices during that time.
After the 3rd quarter of 2023 (end of September), how do prices from 1 Oct. - 31 Dec. reflect net profits? The program will get the highest and lowest prices during that time.
- Second, the blue line is the closing price divided by TTM EPS, which shows the current P/E.
Volatility DashboardThis indicator calculates and displays volatility metrics for a specified number of bars (rolling window) on a TradingView chart. It can be customized to display information in English or Thai and can position the dashboard at various locations on the chart.
Inputs
Language: Users can choose between English ("ENG") and Thai ("TH") for the dashboard's language.
Dashboard Position: Users can specify where the dashboard should appear on the chart. Options include various positions such as "Bottom Right", "Top Center", etc.
Calculation Method: Currently, the script supports "High-Low" for volatility calculation. This method calculates the difference between the highest and lowest prices within a specified timeframe.
Bars: Number of bars used to calculate the volatility.
Display Logic
Fills the islast_vol_points array with the calculated volatility points.
Sets the table cells with headers and corresponding values:
=> Highest Volatility: The maximum value in the islast_vol_points array
=> Mean Volatility: The average value in the islast_vol_points array,
=> Lowest Volatility: The minimum value in the islast_vol_points array, Number of Bars: The rolling window size.
Bull Market Drawdowns V1.0 [ADRIDEM]Bull Market Drawdowns V1.0
Overview
The Bull Market Drawdowns V1.0 script is designed to help visualize and analyze drawdowns during a bull market. This script calculates the highest high price from a specified start date, identifies drawdown periods, and plots the drawdown areas on the chart. It also highlights the maximum drawdowns and marks the start of the bull market, providing a clear visual representation of market performance and potential risk periods.
Unique Features of the New Script
Default Timeframe Configuration: Allows users to set a default timeframe for analysis, providing flexibility in adapting the script to different trading strategies and market conditions.
Customizable Bull Market Start Date: Users can define the start date of the bull market, ensuring the script calculates drawdowns from a specific point in time that aligns with their analysis.
Drawdown Calculation and Visualization: Calculates drawdowns from the highest high since the bull market start date and plots the drawdown areas on the chart with distinct color fills for easy identification.
Maximum Drawdown Tracking and Labeling: Tracks the maximum drawdown for each period and places labels on the chart to indicate significant drawdowns, helping traders identify and assess periods of higher risk.
Bull Market Start Marker: Marks the start of the bull market on the chart with a label, providing a clear reference point for the beginning of the analysis period.
Originality and Usefulness
This script provides a unique and valuable tool by combining drawdown analysis with visual markers and customizable settings. By calculating and plotting drawdowns from a user-defined start date, traders can better understand the performance and risks associated with a bull market. The script’s ability to track and label maximum drawdowns adds further depth to the analysis, making it easier to identify critical periods of market retracement.
Signal Description
The script includes several key visual elements that enhance its usefulness for traders:
Drawdown Area : Plots the upper and lower boundaries of the drawdown area, filling the space between with a semi-transparent color. This helps traders easily identify periods of market retracement.
Maximum Drawdown Labels : Labels are placed on the chart to indicate the maximum drawdown for each period, providing clear markers for significant drawdowns.
Bull Market Start Marker : A label is placed at the start of the bull market, marking the beginning of the analysis period and helping traders contextualize the drawdown data.
These visual elements help quickly assess the extent and impact of drawdowns within a bull market, aiding in risk management and decision-making.
Detailed Description
Input Variables
Default Timeframe (`default_timeframe`) : Defines the timeframe for the analysis. Default is 720 minutes
Bull Market Start Date (`start_date_input`) : The starting date for the bull market analysis. Default is January 1, 2023
Functionality
Highest High Calculation : The script calculates the highest high price on the specified timeframe from the user-defined start date.
```pine
var float highest_high = na
if (time >= start_date)
highest_high := na(highest_high ) ? high : math.max(highest_high , high)
```
Drawdown Calculation : Determines the drawdown starting point and calculates the drawdown percentage from the highest high.
```pine
var float drawdown_start = na
if (time >= start_date)
drawdown_start := na(drawdown_start ) or high >= highest_high ? high : drawdown_start
drawdown = (drawdown_start - low) / drawdown_start * 100
```
Maximum Drawdown Tracking : Tracks the maximum drawdown for each period and places labels above the highest high when a new high is reached.
```pine
var float max_drawdown = na
var int max_drawdown_bar_index = na
if (time >= start_date)
if na(max_drawdown ) or high >= highest_high
if not na(max_drawdown ) and not na(max_drawdown_bar_index) and max_drawdown > 10
label.new(x=max_drawdown_bar_index, y=drawdown_start , text="Max -" + str.tostring(max_drawdown , "#") + "%",
color=color.red, style=label.style_label_down, textcolor=color.white, size=size.normal)
max_drawdown := 0
max_drawdown_bar_index := na
else
if na(max_drawdown ) or drawdown > max_drawdown
max_drawdown := drawdown
max_drawdown_bar_index := bar_index
```
Drawdown Area Plotting : Plots the drawdown area with upper and lower boundaries and fills the area with a semi-transparent color.
```pine
drawdown_area_upper = time >= start_date ? drawdown_start : na
drawdown_area_lower = time >= start_date ? low : na
p1 = plot(drawdown_area_upper, title="Drawdown Area Upper", color=color.rgb(255, 82, 82, 60), linewidth=1)
p2 = plot(drawdown_area_lower, title="Drawdown Area Lower", color=color.rgb(255, 82, 82, 100), linewidth=1)
fill(p1, p2, color=color.new(color.red, 90), title="Drawdown Fill")
```
Current Maximum Drawdown Label : Places a label on the chart to indicate the current maximum drawdown if it exceeds 10%.
```pine
var label current_max_drawdown_label = na
if (not na(max_drawdown) and max_drawdown > 10)
current_max_drawdown_label := label.new(x=bar_index, y=drawdown_start, text="Max -" + str.tostring(max_drawdown, "#") + "%",
color=color.red, style=label.style_label_down, textcolor=color.white, size=size.normal)
if (not na(current_max_drawdown_label))
label.delete(current_max_drawdown_label )
```
Bull Market Start Marker : Places a label at the start of the bull market to mark the beginning of the analysis period.
```pine
var label bull_market_start_label = na
if (time >= start_date and na(bull_market_start_label))
bull_market_start_label := label.new(x=bar_index, y=high, text="Bull Market Start", color=color.blue, style=label.style_label_up, textcolor=color.white, size=size.normal)
```
How to Use
Configuring Inputs : Adjust the default timeframe and start date for the bull market as needed. This allows the script to be tailored to different market conditions and trading strategies.
Interpreting the Indicator : Use the drawdown areas and labels to identify periods of significant market retracement. Pay attention to the maximum drawdown labels to assess the risk during these periods.
Signal Confirmation : Use the bull market start marker to contextualize drawdown data within the overall market trend. The combination of drawdown visualization and maximum drawdown labels helps in making informed trading decisions.
This script provides a detailed view of drawdowns during a bull market, helping traders make more informed decisions by understanding the extent and impact of market retracements. By combining customizable settings with visual markers and drawdown analysis, traders can better align their strategies with the underlying market conditions, thus improving their risk management and decision-making processes.
signalLib_yashgode9Signal Generation Library = "signalLib_yashgode9"
This library, named "signalLib_yashgode9", is designed to generate buy and sell signals based on the price action of a financial instrument. It utilizes various technical indicators and parameters to determine the market direction and provide actionable signals for traders.
Key Features:-
1.Trend Direction Identification: The library calculates the trend direction by comparing the number of bars since the highest and lowest prices within a specified depth. This allows the library to determine the overall market direction, whether it's bullish or bearish.
2.Dynamic Price Tracking: The library maintains two chart points, zee1 and zee2, which dynamically track the price levels based on the identified market direction. These points serve as reference levels for generating buy and sell signals.
3.Customizable Parameters: The library allows users to adjust several parameters, including the depth of the price analysis, the deviation threshold, and the number of bars to consider for the trend direction. This flexibility enables users to fine-tune the library's behavior to suit their trading strategies.
4.Visual Representation: The library provides a visual representation of the buy and sell signals by drawing a line between the zee1 and zee2 chart points. The line's color changes based on the identified market direction, with red indicating a bearish signal and green indicating a bullish signal.
Usage and Integration:
To use this library, you can call the "signalLib_yashgode9" function and pass in the necessary parameters, such as the lower and higher prices, the depth of the analysis, the deviation threshold, and the number of bars to consider for the trend direction. The function will return the direction of the market (1 for bullish, -1 for bearish), as well as the zee1 and zee2 chart points.You can then use these values to generate buy and sell signals in your trading strategy. For example, you could use the direction value to determine when to enter or exit a trade, and the zee1 and zee2 chart points to set stop-loss or take-profit levels.
Potential Use Cases:
This library can be particularly useful for traders who:
1.Trend-following Strategies: The library's ability to identify the market direction can be beneficial for traders who employ trend-following strategies, as it can help them identify the dominant trend and time their entries and exits accordingly.
2.Swing Trading: The dynamic price tracking provided by the zee1 and zee2 chart points can be useful for swing traders, who aim to capture medium-term price movements.
3.Automated Trading Systems: The library's functionality can be integrated into automated trading systems, allowing for the development of more sophisticated and rule-based trading strategies.
4.Educational Purposes: The library can also be used for educational purposes, as it provides a clear and concise way to demonstrate the application of technical analysis concepts in a trading context.
Important Notice:- This library effectively work on timeframe of 5-minute and 15-minute.
Vwap Z-Score with Signals [UAlgo]The "VWAP Z-Score with Signals " is a technical analysis tool designed to help traders identify potential buy and sell signals based on the Volume Weighted Average Price (VWAP) and its Z-Score. This indicator calculates the VWAP Z-Score to show how far the current price deviates from the VWAP in terms of standard deviations. It highlights overbought and oversold conditions with visual signals, aiding in the identification of potential market reversals. The tool is customizable, allowing users to adjust parameters for their specific trading needs.
🔶 Features
VWAP Z-Score Calculation: Measures the deviation of the current price from the VWAP using standard deviations.
Customizable Parameters: Allows users to set the length of the VWAP Z-Score calculation and define thresholds for overbought and oversold levels.
Reversal Signals: Provides visual signals when the Z-Score crosses the specified thresholds, indicating potential buy or sell opportunities.
🔶 Usage
Extreme Z-Score values (both positive and negative) highlight significant deviations from the VWAP, useful for identifying potential reversal points.
The indicator provides visual signals when the Z-Score crosses predefined thresholds:
A buy signal (🔼) appears when the Z-Score crosses above the lower threshold, suggesting the price may be oversold and a potential upward reversal.
A sell signal (🔽) appears when the Z-Score crosses below the upper threshold, suggesting the price may be overbought and a potential downward reversal.
These signals can help you identify potential entry and exit points in your trading strategy.
🔶 Disclaimer
The "VWAP Z-Score with Signals " indicator is designed for educational purposes and to assist traders in their technical analysis. It does not guarantee profitable trades and should not be considered as financial advice.
Users should conduct their own research and use this indicator in conjunction with other tools and strategies.
Trading involves significant risk, and it is possible to lose more than your initial investment.
Composite Risk IndicatorThe Composite Risk Indicator is a financial tool designed to assess market risk by analyzing the spreads between various asset classes. This indicator synthesizes information across six key spreads, normalizing each on a scale from 0 to 100 where higher values represent higher perceived risk. It provides a single, comprehensive measure of market sentiment and risk exposure.
Key Components of the CRI:
1. Stock Market to Bond Market Spread (SPY/BND): Measures the performance of stocks relative to bonds. Higher values indicate stronger stock performance compared to bonds, suggesting increased market optimism and higher risk.
2. Junk Bond to Treasury Bond Spread (HYG/GOVT): Assesses the performance of high-yield (riskier) bonds relative to government (safer) bonds. A higher ratio indicates increased appetite for risk.
3. Junk Bond to Investment Grade Bond Spread (HYG/LQD): Compares high-yield bonds to investment-grade corporate bonds. This ratio sheds light on the risk tolerance within the corporate bond market.
4. Growth to Value Spread (VUG/VTV): Evaluates the performance of growth stocks against value stocks. A higher value suggests a preference for growth stocks, often seen in risk-on environments.
5. Tech to Staples Spread (XLK/XLP): Measures the performance of technology stocks relative to consumer staples. This ratio highlights the market’s risk preference within equity sectors.
6. Small Cap Growth to Small Cap Value Spread (SLYG/SLYV): Compares small-cap growth stocks to small-cap value stocks, providing insight into risk levels in smaller companies.
Utility:
This indicator is particularly useful for investors and traders looking to gauge market sentiment, identify shifts in risk appetite, and make informed decisions based on a broad assessment of market conditions. The CRI can serve as a valuable addition to investment analysis and risk management strategies.
Volatility and Volume by Hour EXT(Extended republication, use this instead of the old one)
The goal of this indicator is to show a “characteristic” of the instrument, regarding the price change and trading volume. You can see how the instrument “behaved” throughout the day in the lookback period. I've found this useful for timing in day trading.
The indicator creates a table on the chart to display various statistics for each hour of the day.
Important: ONLY SHOWS THE TABLE IF THE CHART’S TIMEFRAME IS 1H!
Explanation of the columns:
1. Volatility Percentage (Volat): This column shows the volatility of the price as a percentage. For example, a value of "15%" means the price movement was 15% of the total daily price movement within the hour.
2. Hourly Point Change (PointCh): This column shows the change in price points for each hour in the lookback period. For example, a value of "5" means the price has increased by 5 points in the hour, while "-3" means it has decreased by 3 points.
3. Hourly Point Change Percentage (PrCh% (LeverageX)): This column shows the percentage change in price points for each hour, adjusted with leverage multiplier. Displayed green (+) or red (-) accordingly. For example, a value of "10%" with a leverage of 2X means the price has effectively changed by 5% due to the leverage.
4. Trading Volume Percentage (TrVol): This column shows the percentage of the daily total volume that was traded in a specific hour. For example, a value of "10%" would mean that 10% of the day's total trading volume occurred in that hour.
5. Added New! - Relevancy Check: The indicator checks the last 24 candle. If the direction of the price movement was the same in the last 24 hour as the statistical direction in that hour, the background of the relevant hour in the second column goes green.
For example: if today at 9 o'clock the price went lower, so as at 9 o'clock in the loopback period, the instrument "behaves" according to statistics . So the statistics is probably more relevant for today. The more green background row the more relevancy.
Settings:
1. Lookback period: The lookback period is the number of previous bars from which data is taken to perform calculations. In this script, it's used in a loop that iterates over a certain number of past bars to calculate the statistics. TIP: Select a period the contains a trend in one direction, because an upward and a downward trend compensate the price movement in opposite directions.
2. Timezone: This is a string input that represents the user's timezone. The default value is "UTC+2". Adjust it to your timezone in order to view the hours properly.
3. Leverage: The default value is 10(!). This input is used to adjust the hourly point change percentage. For FOREX traders (for example) the statistics can show the leveraged percentage of price change. Set that according the leverage you trade the instrument with.
Use at your own risk, provided “as is” basis!
Hope you find it useful! Cheers!
trend_switch
█ Description
Asset price data was time series data, commonly consisting of trends, seasonality, and noise. Many applicable indicators help traders to determine between trend or momentum to make a better trading decision based on their preferences. In some cases, there is little to no clear market direction, and price range. It feels much more appropriate to use a shorter trend identifier, until clearly defined market trend. The indicator/strategy developed with the notion aims to automatically switch between shorter and longer trend following indicator. There were many methods that can be applied and switched between, however in this indicator/strategy will be limited to the use of predictive moving average and MESA adaptive moving average (Ehlers), by first determining if there is a strong trend identified by calculating the slope, if slope value is between upper and lower threshold assumed there is not much price direction.
█ Formula
// predictive moving average
predict = (2*wma1-wma2)
trigger = (4*predict+3*predict +2*predict *predict)
// MESA adaptive moving average
mama = alpha*src+(1-alpha)*mama
fama = .5*alpha*mama+(1-.5-alpha)*fama
█ Feature
The indicator will have a specified default parameter of:
source = ohlc4
lookback period = 10
threshold = 10
fast limit = 0.5
slow limit = 0.05
Strategy type can be switched between Long/Short only and Long-Short strategy
Strategy backtest period
█ How it works
If slope between the upper (red) and lower (green) threshold line, assume there is little to no clear market direction, thus signal predictive moving average indicator
If slope is above the upper (red) or below the lower (green) threshold line, assume there is a clear trend forming, the signal generated from the MESA adaptive moving average indicator
█ Example 1 - Slope fall between the Threshold - activate shorter trend
█ Example 2 - Slope fall above/below Threshold - activate longer trend
Normalized Z-ScoreThe Normalized Z-Score indicator is designed to help traders identify overbought or oversold conditions in a security's price. This indicator can provide valuable signals for potential buy or sell opportunities by analyzing price deviations from their average values.
How It Works :
-- Z-Score Calculation:
---- The indicator calculates the Z-Score for both high and low prices over a user-defined period (default is 14 periods).
---- The Z-Score measures how far a price deviates from its average in terms of standard deviations.
-- Average Z-Score:
---- The average Z-Score is derived by taking the mean of the high and low Z-Scores.
-- Normalization:
---- The average Z-Score is then normalized to a range between -1 and 1. This helps in standardizing the indicator's values, making it easier to interpret.
-- Signal Line:
---- A signal line, which is the simple moving average (SMA) of the normalized Z-Score, is calculated to smooth out the data and highlight trends.
-- Color-Coding:
---- The signal line changes color based on its value: green when it is positive (indicating a potential buy signal) and red when it is negative (indicating a potential sell signal). This coloration is also used for the candle/bar coloration.
How to Use It:
-- Adding the Indicator:
---- Add the Normalized Z-Score indicator to your TradingView chart. It will appear in a separate pane below the price chart.
-- Interpreting the Histogram:
---- The histogram represents the normalized Z-Score. High positive values suggest overbought conditions, while low negative values suggest oversold conditions.
-- Using the Signal Line:
---- The signal line helps to confirm the conditions indicated by the histogram. A green signal line suggests a potential buying opportunity, while a red signal line suggests a potential selling opportunity.
-- Adjusting the Period:
---- You can adjust the period for the Z-Score calculation to suit your trading strategy. The default period is 14, but you can change this based on your preference.
Example Scenario:
-- Overbought Condition: If the histogram shows a high positive value and the signal line is green, the security may be overbought. This could indicate that it is a good time to consider selling.
-- Oversold Condition: If the histogram shows a low negative value and the signal line is red, the security may be oversold. This could indicate that it is a good time to consider buying.
By using the Normalized Z-Score indicator, traders can gain insights into price deviations and potential market reversals, aiding in making more informed trading decisions.
Alert Before Bar Closei.imgur.com
Alert Before Bar Close
==========================
Example Figure
Originality and usefulness
This indicator/alert mechanism is unique in two ways. First, it provides alerts before the close of a candlestick, allowing time-based traders to prepare early to determine if the market is about to form a setup. Second, it introduces an observation time mechanism, enabling time-based traders to observe when the market is active, thereby avoiding too many false signals during electronic trading or when trading is light.
Detail
Regarding the settings (Arrow 1). The first input is to select the candlestick period you want to observe. The second is to notify a few seconds in advance. The third input sets the observation time. For example, if you set "1,2,3,4,5," the alert mechanism will only be activated during the period from 01:00:00 to 05:59:59, consistent with the time zone you set in TradingView. Additionally, I have set it so that the alert will only trigger once per candlestick, so don't worry about repeated alerts.
The alert setup is very simple, too. Follow the steps (Arrow 2, 3) to complete the setup. I have tested several periods and successfully received alerts on both mobile and computer. If anyone encounters any issues, feel free to let me know.
Seasonality Widget [LuxAlgo]The Seasonality Widget tool allows users to easily visualize seasonal trends from various data sources.
Users can select different levels of granularity as well as different statistics to express seasonal trends.
🔶 USAGE
Seasonality allows us to observe general trends occurring at regular intervals. These intervals can be user-selected from the granularity setting and determine how the data is grouped, these include:
Hour
Day Of Week
Day Of Month
Month
Day Of Year
The above seasonal chart shows the BTCUSD seasonal price change for every hour of the day, that is the average price change taken for every specific hour. This allows us to obtain an estimate of the expected price move at specific hours of the day.
Users can select when data should start being collected using the "From Date" setting, any data before the selected date will not be included in the calculation of the Seasonality Widget.
🔹 Data To Analyze
The Seasonality Widget can return the seasonality for the following data:
Price Change
Closing price minus the previous closing price.
Price Change (%)
Closing price minus the previous closing price, divided by the
previous closing price, then multiplied by 100.
Price Change (Sign)
Sign of the price change (-1 for negative change, 1 for positive change), normalized in a range (0, 100). Values above 50 suggest more positive changes on average.
Range
High price minus low price.
Price - SMA
Price minus its simple moving average. Users can select the SMA period.
Volume
Amount of contracts traded. Allow users to see which periods are generally the most /least liquid.
Volume - SMA
Volume minus its simple moving average. Users can select the SMA period.
🔹 Filter
In addition to the "From Date" threshold users can exclude data from specific periods of time, potentially removing outliers in the final results.
The period type can be specified in the "Filter Granularity" setting. The exact time to exclude can then be specified in the "Numerical Filter Input" setting, multiple values are supported and should be comma separated.
For example, if we want to exclude the entire 2008 period we can simply select "Year" as filter granularity, then input 2008 in the "Numerical Filter Input" setting.
Do note that "Sunday" uses the value 1 as a day of the week.
🔶 DETAILS
🔹 Supported Statistics
Users can apply different statistics to the grouped data to process. These include:
Mean
Median
Max
Min
Max-Min Average
Using the median allows for obtaining a measure more robust to outliers and potentially more representative of the actual central tendency of the data.
Max and Min do not express a general tendency but allow obtaining information on the highest/lowest value of the analyzed data for specific periods.
🔶 SETTINGS
Granularity: Periods used to group data.
From Data: Starting point where data starts being collected
🔹 Data
Analyze: Specific data to be processed by the seasonality widget.
SMA Length: Period of the simple moving average used for "Price - SMA" and "Volume - SMA" options in "Analyze".
Statistic: Statistic applied to the grouped data.
🔹 Filter
Filter Granularity: Period type to exclude in the processed data.
Numerical Filter Input: Determines which of the selected hour/day of week/day of month/month/year to exclude depending on the selected Filter Granularity. Only numerical inputs can be provided. Multiple values are supported and must be comma-separated.
Profitability Power RatioProfitability Power Ratio
The Profitability Power Ratio is a financial metric designed to assess the efficiency of a company's operations by evaluating the relationship between its Enterprise Value (EV) and Return on Equity (ROE). This ratio provides insights into how effectively a company generates profits relative to its equity and overall valuation.
Qualities and Interpretations:
1. Efficiency Benchmark: The Profitability Power Ratio serves as a benchmark for evaluating how efficiently a company utilizes its equity capital to generate profits. A higher ratio indicates that the company is generating significant profits relative to its valuation, reflecting efficient use of invested capital.
2. Financial Health Indicator: This ratio can be used as an indicator of financial health. A consistently high or improving ratio over time suggests strong operational efficiency and sustainable profitability.
3. Investment Considerations: Investors can use this ratio to assess the attractiveness of an investment opportunity. A high ratio may signal potential for good returns, but it's important to consider the underlying reasons for the ratio's level to avoid misinterpretation.
4. Risk Evaluation: An excessively high Profitability Power Ratio could also signal elevated risk. It may indicate aggressive financial leveraging or unsustainable growth expectations, which could pose risks during economic downturns or market fluctuations.
Interpreting the Ratio:
1. Higher Ratio: A higher Profitability Power Ratio typically signifies efficient capital utilization and strong profitability relative to the company's valuation.
2. Lower Ratio: A lower ratio may suggest inefficiencies in capital allocation or lower profitability relative to enterprise value.
3. Benchmarking: Compare the company's ratio with industry peers and historical performance to gain deeper insights into its financial standing and operational efficiency.
Using the Indicator:
The Profitability Power Ratio is plotted on a chart to visualize trends and fluctuations over time. Users can customize the color of the plot to emphasize this metric and integrate it into their financial analysis toolkit for comprehensive decision-making.
Disclaimer: The Profitability Power Ratio is a financial metric designed for informational purposes only and should not be considered as financial or investment advice. Users should conduct thorough research and analysis before making any investment decisions based on this indicator. Past performance is not indicative of future results. All investments involve risks, and users are encouraged to consult with a qualified financial advisor or professional before making investment decisions.
Dividend-to-ROE RatioDividend-to-ROE Ratio Indicator
The Dividend-to-ROE Ratio indicator offers valuable insights into a company's dividend distribution relative to its profitability, specifically comparing the Dividend Payout Ratio (proportion of earnings as dividends) to the Return on Equity (ROE), a measure of profitability from shareholder equity.
Interpretation:
1. Higher Ratio: A higher Dividend-to-ROE Ratio suggests a stable dividend policy, where a significant portion of earnings is returned to shareholders. This can indicate consistent dividend payments, often appealing to income-seeking investors.
2. Lower Ratio: Conversely, a lower ratio implies that the company retains more earnings for growth, potentially signaling a focus on reinvestment for future expansion rather than immediate dividend payouts.
3. Excessively High Ratio: An exceptionally high ratio may raise concerns. While it could reflect a generous dividend policy, excessively high ratios might indicate that a company is distributing more earnings than it can sustainably afford. This could potentially hinder the company's ability to reinvest in its operations, research, or navigate economic downturns effectively.
Utility and Applications:
The Dividend-to-ROE Ratio can be particularly useful in the following scenarios:
1. Income-Oriented Investors: For investors seeking consistent dividend income, a higher ratio signifies a company's commitment to distributing profits to shareholders, potentially aligning with income-oriented investment strategies.
2. Financial Health Assessment: Analysts and stakeholders can use this ratio to gauge a company's financial health and dividend sustainability. It provides insights into management's capital allocation decisions and strategic focus.
3. Comparative Analysis: When comparing companies within the same industry, this ratio helps in benchmarking dividend policies and identifying outliers with unusually high or low ratios.
Considerations:
1. Contextual Analysis: Interpretation should be contextualized within industry standards and the company's financial history. Comparing the ratio with peers in the same sector can provide meaningful insights.
2. Financial Health: It's crucial to evaluate this indicator alongside other financial metrics (like cash flow, debt levels, and profit margins) to grasp the company's overall financial health and sustainability of its dividend policy.
Disclaimer: This indicator is for informational purposes only and does not constitute financial advice. Investors should conduct thorough research and consult with financial professionals before making investment decisions based on this ratio.
Fourier Adjusted Average True Range [BackQuant]Fourier Adjusted Average True Range
1. Conceptual Foundation and Innovation
The FA-ATR leverages the principles of Fourier analysis to dissect market prices into their constituent cyclical components. By applying Fourier Transform to the price data, the FA-ATR captures the dominant cycles and trends which are often obscured in noisy market data. This integration allows the FA-ATR to adapt its readings based on underlying market dynamics, offering a refined view of volatility that is sensitive to both market direction and momentum.
2. Technical Composition and Calculation
The core of the FA-ATR involves calculating the traditional ATR, which measures market volatility by decomposing the entire range of price movements. The FA-ATR extends this by incorporating a Fourier Transform of price data to assess cyclical patterns over a user-defined period 'N'. This process synthesizes both the magnitude of price changes and their rhythmic occurrences, resulting in a more comprehensive volatility indicator.
Fourier Transform Application: The Fourier series is calculated using price data to identify the fundamental frequency of market movements. This frequency helps in adjusting the ATR to reflect more accurately the current market conditions.
Dynamic Adjustment: The ATR is then adjusted by the magnitude of the dominant cycle from the Fourier analysis, enhancing or reducing the ATR value based on the intensity and phase of market cycles.
3. Features and User Inputs
Customizability: Traders can modify the Fourier period, ATR period, and the multiplication factor to suit different trading styles and market environments.
Visualization : The FA-ATR can be plotted directly on the chart, providing a visual representation of volatility. Additionally, the option to paint candles according to the trend direction enhances the usability and interpretative ease of the indicator.
Confluence with Moving Averages: Optionally, a moving average of the FA-ATR can be displayed, serving as a confluence factor for confirming trends or potential reversals.
4. Practical Applications
The FA-ATR is particularly useful in markets characterized by periodic fluctuations or those that exhibit strong cyclical trends. Traders can utilize this indicator to:
Adjust Stop-Loss Orders: More accurately set stop-loss orders based on a volatility measure that accounts for cyclical market changes.
Trend Confirmation: Use the FA-ATR to confirm trend strength and sustainability, helping to avoid false signals often encountered in volatile markets.
Strategic Entry and Exit: The indicator's responsiveness to changing market dynamics makes it an excellent tool for planning entries and exits in a trend-following or a breakout trading strategy.
5. Advantages and Strategic Value
By integrating Fourier analysis, the FA-ATR provides a volatility measure that is both adaptive and anticipatory, giving traders a forward-looking tool that adjusts to changes before they become apparent through traditional indicators. This anticipatory feature makes it an invaluable asset for traders looking to gain an edge in fast-paced and rapidly changing market conditions.
6. Summary and Usage Tips
The Fourier Adjusted Average True Range is a cutting-edge development in technical analysis, offering traders an enhanced tool for assessing market volatility with increased accuracy and responsiveness. Its ability to adapt to the market's cyclical nature makes it particularly useful for those trading in highly volatile or cyclically influenced markets.
Traders are encouraged to integrate the FA-ATR into their trading systems as a supplementary tool to improve risk management and decision-making accuracy, thereby potentially increasing the effectiveness of their trading strategies.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
CAPEX RatioUnderstanding the CAPEX Ratio: An Essential Financial Metric
Introduction
In the world of finance, understanding how companies allocate their resources and reinvest their earnings is crucial for investors and analysts. One fundamental metric used to assess a company's investment behavior is the CAPEX Ratio. This article delves into what the CAPEX Ratio signifies, its advantages, and how to interpret its implications.
What is the CAPEX Ratio?
The CAPEX Ratio, short for Capital Expenditure Ratio, is a financial indicator that measures the proportion of a company's capital expenditures (CAPEX) relative to various financial metrics such as revenue, free cash flow, net income, or total assets. CAPEX represents investments made by a company to acquire or maintain its physical assets.
Interpreting the Results
Each variant of the CAPEX Ratio provides unique insights into a company's financial strategy:
• CAPEX to Revenue Ratio: This ratio shows what portion of a company's revenue is being reinvested into capital investments. A higher ratio might indicate aggressive expansion plans or a need for infrastructure upgrades.
• CAPEX to Free Cash Flow Ratio: By comparing CAPEX with free cash flow, this ratio reveals how much of a company's available cash is dedicated to capital investments. It helps assess financial health and sustainability.
• CAPEX to Net Income Ratio: This ratio measures how much of a company's net income is being channeled back into capital expenditures. A high ratio relative to net income could signal a company's commitment to growth and development.
• CAPEX to Total Assets Ratio: This metric assesses the proportion of total assets being allocated towards capital expenditures. It provides a perspective on the company's investment intensity relative to its overall asset base.
Advantages of Using CAPEX Ratios
• Insight into Investment Strategy: Helps investors understand where a company is directing its resources.
• Evaluation of Financial Health: Indicates how efficiently a company is reinvesting profits or available cash.
• Comparative Analysis: Enables comparisons across companies or industries to gauge investment priorities.
How to Use the CAPEX Ratio
• Comparative Analysis: Compare the CAPEX Ratios over time or against industry peers to spot trends or outliers.
• Investment Decision-Making: Consider CAPEX Ratios alongside other financial metrics when making investment decisions.
Conclusion
In conclusion, the CAPEX Ratio is a valuable financial metric that offers deep insights into a company's investment behavior and financial health. By analyzing different variants of this ratio, investors and analysts can make informed decisions about a company's growth prospects and financial stability.
Useful_lib_publicLibrary "Useful_lib_public"
Useful functions
CountBarsOfDay()
count bars for one for the diffrent time frames
Returns: number of bars for one day
LastBarsOfDay()
Index number for the las bar for one day
Returns: TRUE is that the last bar from day
isTuesday()
TRUE is tuesday
Returns: TRUE is tuesday else FALSE
Rsi(src, len)
RSI calulation
Parameters:
src (float) : RSI Source
len (simple int) : RSI Length
Returns: RSI Value
CalcIndex(netPos, weeks)
Index calulation
Parameters:
netPos (float) : Source
weeks (simple int) : Length
Returns: "COT Index"
RsiStock(src, len, smoothK)
TRUE is tuesday
Parameters:
src (float)
len (simple int)
smoothK (int)
Returns: RSI Stochastik
Offset()
Use Offset for Day time frame
Returns: Offset
PercentChange(Data, LastData)
Calc different in Percent
Parameters:
Data (float)
LastData (float)
Returns: Change in percent
