BTC Day Trading Strategy with Alerts(LAWY2024)🏆 Best Indicator for BTC Day Trading:
I recommend using Exponential Moving Averages (EMAs) + RSI + VWAP + ATR for a well-rounded approach.
✅ Main Indicator: 9 & 21 EMA Crossover (Momentum & Trend Confirmation)
How It Works:
When the 9 EMA crosses above the 21 EMA, it signals a potential buy (bullish momentum).
When the 9 EMA crosses below the 21 EMA, it signals a potential sell (bearish momentum).
Why?: EMAs react quickly to price changes, making them perfect for day trading.
✅ Support Indicator: VWAP (Volume-Weighted Average Price)
How It Works:
Price above VWAP = bullish (only look for longs).
Price below VWAP = bearish (only look for shorts).
Why?: Institutions & large traders use VWAP to gauge fair value for intraday moves.
✅ Momentum Confirmation: RSI (Relative Strength Index, 14)
Overbought (>70) = Look for short setups.
Oversold (<30) = Look for long setups.
Best Use: Look for bullish or bearish divergences to confirm trend reversals.
✅ Risk Management: ATR (Average True Range, 14)
Helps determine stop-loss placement based on volatility.
Example: If ATR is $500, set SL at 1x or 1.5x ATR to avoid getting stopped out by normal BTC fluctuations.
📌 How This Script Works
✅ Buys when:
9 EMA crosses above 21 EMA (bullish momentum).
Price is above VWAP (institutional bias is bullish).
RSI is above 50 (confirming bullish momentum).
Sets SL at ATR x 1.5 below entry, TP at 2x SL.
✅ Sells when:
9 EMA crosses below 21 EMA (bearish momentum).
Price is below VWAP (institutional bias is bearish).
RSI is below 50 (confirming bearish momentum).
Sets SL at ATR x 1.5 above entry, TP at 2x SL.
Cycles
Grok's Enhanced Volatility-Adjusted MomentumThis indicator combines traditional momentum analysis with volatility adjustment to provide clearer, more reliable trading signals. It uses a unique approach by normalizing momentum with the Average True Range (ATR) to account for varying market conditions. The indicator also incorporates trend confirmation through two Simple Moving Averages (SMA) and leverages RSI for additional momentum validation.
Key Features:
Volatility-Adjusted Momentum: Momentum readings are scaled by current market volatility, offering signals that adapt to market conditions.
Trend Confirmation: Utilizes short (11-period) and long (50-period) SMAs to ensure signals align with the broader market trend.
RSI Integration: Adds an RSI threshold to confirm momentum, reducing false signals in overbought or oversold territories.
Signal Visualization: Clear "Strong Buy" and "Strong Sell" labels appear on the chart, making it easy to spot trading opportunities.
GKF 22 EMA Crossover StrategyThe script calculates the 22 EMA and generates buy/sell signals based on price crossing above or below the EMA.
It uses swing highs and lows for profit targets and stop losses.
Buy signals are plotted as green labels below the bars, and sell signals are plotted as red labels above the bars.
BankNifty Buy/Sell Indicator//@version=5
strategy("BankNifty Buy/Sell Indicator", overlay=true)
// Input Parameters
emaLength = input(20, title="EMA Length")
rsiLength = input(14, title="RSI Length")
macdFast = input(12, title="MACD Fast Length")
macdSlow = input(26, title="MACD Slow Length")
macdSignal = input(9, title="MACD Signal Length")
// Indicators Calculation
ema = ta.ema(close, emaLength)
rsi = ta.rsi(close, rsiLength)
= ta.macd(close, macdFast, macdSlow, macdSignal)
// Buy & Sell Conditions
buyCondition = ta.crossover(macdLine, signalLine) and rsi < 30 and close > ema
sellCondition = ta.crossunder(macdLine, signalLine) and rsi > 70 and close < ema
// Plot Buy & Sell Signals
plotshape(buyCondition, location=location.belowbar, color=color.green, style=shape.labelup, title="BUY")
plotshape(sellCondition, location=location.abovebar, color=color.red, style=shape.labeldown, title="SELL")
// Strategy Execution
strategy.entry("BUY", strategy.long, when=buyCondition)
strategy.close("BUY", when=sellCondition)
Liquidity alertThis indicator gives you alerts when session liquidity is sweped. Nothing more, nothing less
Fibonacci Levels and EMAsTradingView Pine Script error explanation – “The 'undeclared identifier' error happens when TradingView cannot find the variable or function that we try to use.”
TRADINGCODE.NET
Pine Script scope and assignment solution – Define variables in global scope and use := inside if blocks
STACKOVERFLOW.COM
. This prevents “undeclared identifier” errors for variables set in conditional logic.
Pine Script plotting rules – “You can't use plot statements in ... any other local block in a script.” (must be global)
TOUCH-DT.COM
.
Example of invalid plot parameter causing error – using an unsupported 'dashed' style in plot() led to an undeclared identifier issue
REDDIT.COM
. Always use valid plot styles or drawing functions for custom line styles.
Fibonacci level calculation example – computing fib retracement levels from a high and low price
TRADINGVIEW.COM
(standard 23.6%, 38.2%, 50%, 61.8% formula).
ORB with 100 EMAORB Trading Strategy for FX Pairs on the 30-Minute Time Frame
Overview
This Opening Range Breakout (ORB) strategy is designed for trading FX pairs on the 30-minute time frame. The strategy is structured to take advantage of price momentum while aligning trades with the overall trend using the 100-period Exponential Moving Average (100EMA). The primary objective is to enter trades when price breaks and closes above or below the Opening Range (OR), with additional confirmation from a retest of the OR level if the initial entry is missed.
Strategy Rules
1. Defining the Opening Range (OR)
- The OR is determined by the high and low of the first 30-minute candle after market open.
- This range acts as the key level for breakout trading.
2. Trend Confirmation Using the 100EMA
- The 100EMA serves as a filter to determine trade direction:
- Buy Setup: Only take buy trades when the OR is above the 100EMA.
- Sell Setup: Only take sell trades when the OR is below the 100EMA.
3. Entry Criteria
- Buy Trade: Enter a long position when a candle breaks and closes above the OR high, confirming the breakout.
- Sell Trade: Enter a short position when a candle breaks and closes below the OR low, confirming the breakout.
- Retest Entry: If the initial entry is missed, wait for a price retest of the OR level for a secondary entry opportunity.
4. Risk-to-Reward Ratio (R2R)
- The goal is to target a 1:1 Risk-to-Reward (R2R) ratio.
- Stop-loss placement:
- Buy Trade: Place stop-loss just below the OR low.
- Sell Trade: Place stop-loss just above the OR high.
- Take profit at a distance equal to the stop-loss for a 1:1 R2R.
5. Risk Management
- Risk per trade should be based on personal risk tolerance.
- Adjust lot sizes accordingly to maintain a controlled risk percentage of account balance.
- Avoid over-leveraging, and consider moving stop-loss to breakeven if the price moves favourably.
Additional Considerations
- Avoid trading during major news events that may cause high volatility and unpredictable price movements.
- Monitor market conditions to ensure breakout confirmation with strong momentum rather than false breakouts.
- Use additional confluences such as candlestick patterns, support/resistance zones, or volume analysis for stronger trade validation.
This ORB strategy is designed to provide structured trade opportunities by combining breakout momentum with trend confirmation via the 100EMA. The strategy is straightforward, allowing traders to capitalise on clear breakout movements while implementing effective risk management practices. While the 1:1 R2R target provides a balanced approach, traders should always adapt their risk tolerance and market conditions to optimise trade performance.
By following these rules and maintaining discipline, traders can use this strategy effectively across various FX pairs on the 30-minute time frame.
Shifted Buy PressureDifferentiated Buy Pressure Indicator Documentation
Overview: The Differentiated Buy Pressure indicator is a custom Pine Script™ indicator designed to measure and visualize buy and sell pressure in the market. It calculates buy pressure based on a combination of volume, range, and gap, and provides a differentiated buy pressure which is shifted by 90°, offering predictive insights.
Inputs:
Window Size: The window size for average calculation (default: 20).
Show Overlay: Option to show the price overlay (default: false).
Overlay Boost Factor: Boosting factor for overlaying the price (default: 0.01).
Calculations:
Relative Range: Calculated as (high - low) / close.
Average Range: Simple moving average of the relative range over the specified window.
Average Volume: Simple moving average of the volume over the specified window.
Relative Gap: Calculated as open / close .
Average Gap: Simple moving average of the relative gap over the specified window.
Buy Pressure: Calculated using the formula: buy_pressure = -math.log(relative_range / avg_range * volume / avg_volume * relative_gap / avg_gap)
Differentiated Buy Pressure: Calculated as the difference between the current and previous buy pressure: diff_buy_pressure = buy_pressure - buy_pressure
Plots:
Zero Line: A horizontal line at zero for reference.
Buy Pressure: Plotted in blue, representing the calculated buy pressure.
Differentiated Buy Pressure: Plotted in red, representing the differentiated buy pressure.
Overlay: Optionally plots the price overlay boosted by the differentiated buy pressure.
Labels:
Labels are created to display the buy pressure and differentiated buy pressure values on the chart.
Usage: This indicator helps traders visualize the buy and sell pressure in the market. Positive values indicate buy pressure, while negative values indicate sell pressure. The differentiated buy pressure, shifted by 90°, provides predictive insights into future market movements.
This documentation provides a comprehensive overview of the Differentiated Buy Pressure indicator, explaining its purpose, inputs, calculations, and usage.
*VIP*Trend Lines, Breakouts, Triangles, Volume*VIP*Script Overview
This script provides an automated analysis of price action by identifying key technical patterns and signals. It includes trend lines, breakout alerts, triangle formations, volume analysis, candlestick patterns, and Fibonacci levels to assist with trading decisions.
Features & Functionality
📈 Trend Lines : A sloping trend line is drawn from the first bar, extending to the right. Price breakouts above or below this line are automatically flagged.
🚀 Breakout Patterns : When the price crosses the trend line, a “Take Profit Here” label is placed to highlight potential trading opportunities.
🔺 Triangle Patterns : Simulated triangle formations are drawn using horizontal lines to mark the top and bottom boundaries.
📊 Volume Analysis : Volume spikes are detected when the trading volume exceeds twice the 20-period SMA of volume.
🕯 Candlestick Patterns : Key reversal patterns like bullish and bearish engulfing formations are identified and marked on the chart.
📌 Support & Resistance Levels : Major price levels are automatically identified and plotted as dotted lines.
📐 Fibonacci Levels : Fibonacci retracement and extension levels are calculated and displayed for trend-based analysis.
How to Use
1️⃣ Add the script to your TradingView chart.
2️⃣ Adjust parameters (e.g., trend line slope, Fibonacci levels) to fit your trading strategy.
3️⃣ Look for signals like breakout alerts, volume spikes, and candlestick patterns to guide your trading decisions.
4️⃣ Customize further if needed—this script serves as a foundation for your personalized trading analysis.
If you have suggestions i am open to them.
OmniPulse (Fixed Version)OmniPulse (Fixed Version) – Description
OmniPulse is a multi-indicator framework designed to combine three core oscillators—RSI, Stochastic, and Momentum—at various lookback lengths, then refine their signals using placeholder features such as machine learning forecasting, adaptive cycle detection, and neural network filtering. While some of these advanced features are not natively supported in Pine Script, they are represented here in simplified forms to illustrate how a more sophisticated system could be structured.
Key Components:
Multi-Length Oscillator Arrays
RSI (calcrsi() function)
Stochastic (placeholder via ta.sma() on a typical price average)
Momentum (ta.roc())
These are calculated for multiple lengths defined by the rsiLengths, stochLengths, and momentumLengths arrays.
Dual-Threshold Convergence
Compares each oscillator’s value to user-defined upper/lower thresholds (threshold1, threshold2) to identify bullish or bearish conditions.
Summarizes results in a convergence score.
Placeholder Machine Learning Forecast
Demonstrates a simple averaging of oscillator values as a “forecast” when toggled on.
Adaptive Cycle Detection (Placeholder)
Introduces a static cycle period (e.g., 20.0) as a placeholder for more advanced transforms.
Neural Network Filter (Placeholder)
Averages convergence, forecast, and cyclePeriod into a single filteredSignal.
Signal Plotting
Plots the filtered signal on the chart.
Highlights potential bullish or bearish extremes with shape markers based on percentile thresholds.
Practical Use & Extension:
Real Multi-Timeframe Analysis: Replace placeholders with request.security() for each timeframe.
Advanced Forecasting: Incorporate custom or external machine learning models.
Genuine Cycle Detection: Implement more sophisticated logic or user-defined cycle detection tools.
Neural Network Heuristics: Expand the placeholder step into a deeper filtering or weighting system.
Overall, OmniPulse serves as an adaptable blueprint for traders and developers, showcasing how multiple indicators and advanced concepts might be combined into a cohesive, signal-generating framework.
Bitcoin Logarithmic Regression Channel1. Introduction
Bitcoin’s price had historically exhibited exponential growth, meaning its growth rate increased over time rather than remaining constant. To analyze this behavior, I employed a logarithmic scale. The logarithmic transformation converted the exponential growth pattern into a linear relationship, which was easier to analyze using linear regression techniques.
2. What Is a Logarithmic Function?
A logarithmic function (in this case, using base 10) transforms a number x into log10(x). For example, log10(100) = 2 because 10^2 = 100. This transformation compresses data that spans several orders of magnitude into a smaller, more manageable scale. In my analysis, the logarithmic transformation was applied to both the time variable (expressed as “weekly bar numbers”) and the price. This approach converted an exponential relationship into a linear one.
3. Selection of Key Coordinates
Key turning points in Bitcoin’s historical price were selected, representing the cycle peaks and bottoms. Since TradingView charts begin on 2009 10 05, I converted these dates into “weekly bar numbers. The chosen coordinates were as follows:
- Cycle Peaks (Highs):
(2011-06 06-$32) -> (80, $32)
(2013-11-25, $1240) -> (208, $1240)
(2017-12-11, $19804) -> (451, $19804)
(2021-11-08, $68997) -> (623, $68997)
- Cycle Bottoms (Lows):
(2011-11-21, $2) -> (102, $2)
(2015-08-17, $162) -> (298, $162)
(2018-12-10, $3124) -> (471, $3124)
(2022-11-21, $15473) -> (677, $15473)
These points were chosen because they marked significant turning points in Bitcoin’s price cycles.
4. Conversion to Linear Form
The values then were converted to their linear form by applying the base 10 logarithm. This transformation allowed the function to be expressed in a linear state as:
y = a * x + b
where:
- x is the logarithm of the weekly bar number, and
- y is the logarithm of the price.
This step was essential for enabling linear regression on these values. After applying the base 10 logarithm, the coordinates became:
- Cycle Peaks (Highs):
(1.903, 1.505)
(2.318, 3.093)
(2.654, 4.296)
(2.795, 4.839)
- Cycle Bottoms (Lows):
(2.009, 0.301)
(2.474, 2.210)
(2.673, 3.494)
(2.830, 4.190)
5. Linear Regression
A linear regression was then performed on these log transformed points separately for the highs and lows. The best fit lines obtained were:
- Cycle Peaks (Highs):
y = 3.711 * x – 5.541
In exponential form, this translates to:
Price = 10^(3.711 * log(x)-5.541)
- Cycle Bottoms (Lows):
y = 4.782 * x – 9.385
In exponential form, this translates to:
Price = 10^(4.782 * log(x)-9.385)
6. Visualization through Log Channel with Offsets
To provide additional context and to visually assess potential overextensions or undervaluations relative to the long term trend, channels were drawn around the regression lines by applying a percentage offset. For example, with a 20% offset:
- Upper Channel:
Upper=Regression Value * (1 + 0.2)
- Lower Channel:
Lower=Regression Value * (1 - 0.2)
These channels help illustrate how far the current price deviates from the trend line and can signal potential reversal points if the price moves significantly outside these boundaries.
7. Meaning and Application of the Model
This model was designed to analyze Bitcoin’s long term trend by:
• Assessing Market Conditions:By comparing the current Bitcoin price to the regression channels, one can gauge whether Bitcoin is trading above (potentially overextended) or below (potentially undervalued) its long term trend.
• Forecasting Reversal Points: Significant deviations from the channel boundaries may indicate that a price correction or reversal is imminent.
• Supporting Investment Decisions: The regression function, together with its channels, provides a quantitative framework for evaluating the current market state and estimating future price targets, helping to guide strategic entry or exit decisions.
Based on the derived model and current market conditions, today's analysis indicates that Bitcoin's support levels lie approximately between $24,000 and $36,000, while its resistance levels are in the range of about $134,000 to $202,000.
8. Summary
This model came together by pinpointing key turning points in Bitcoin’s price history and converting the dates into weekly bar numbers. I then applied a base 10 logarithm to both the time and price data, which transformed Bitcoin’s exponential growth into a straight-line relationship. Using linear regression on these log transformed values, I derived trend equations for the peaks and bottoms, and added channels with a set offset to highlight areas of potential support and resistance. Overall, this approach offers a clear, data-driven way to gauge Bitcoin’s long-term trend and anticipate potential market turning points.
1. 서론
비트코인은 창시 이후 상상할 수 없을 정도의 상승세를 보여주었으며, 그 폭발적인 성장률은 시간이 흐를수록 걷잡을 수 없이 가속화되었습니다. 이러한 현상은 단순한 우연이 아니라, 전 세계 투자자들과 주요 기관들의 열렬한 관심을 이끌어내며 비트코인이 주요 자산으로 자리잡게 된 배경이 되었습니다. 저는 최근에 이 비범한 상승세의 이면을 보다 깊이 이해하고자 로그 기반으로 간단한 비트코인 추세 채널링 모델을 만들어봤습니다.
2. 로그함수란?
로그함수는 어떤 숫자를, 특정 밑을 기준으로 그 크기를 지수 형태로 표현해 주는 함수입니다. 예를 들어, 100이라는 숫자는 10의 몇 제곱인지 나타내면 10^2 = 100이므로, log(100)는 2가 됩니다. 즉, 로그함수는 아주 크거나 작은 값을 다루기 쉽게 숫자 범위를 압축해 주는 역할을 합니다. 제 분석에서는 ‘주간 바 번호’라는 시간 변수와 가격 데이터에 모두 로그 변환을 적용하여, 본래 기하급수적으로 증가하는 데이터를 선형 관계로 바꿔서 보다 쉽게 이해하고 분석할 수 있도록 하였습니다. 참고로 본 분석에서는 로그 10진법을 사용하였습니다.
3. 주요 고점/저점 선택
역대 비트코인의 가격 사이클에서 중요한 변곡점들을 고점 저점 각각 4개씩 도출했습니다. 트레이딩뷰의 BTCUSD:INDEX 차트는 2009년 10월 05일부터 시작되므로, 각 날짜가 차트 상에서 몇 번째 봉에 해당하는지를 계산하였습니다. 좌표는 다음과 같습니다:
사이클 상단 (고점):
(2011-06 06-$32) -> (80, $32)
(2013-11-25, $1240) -> (208, $1240)
(2017-12-11, $19804) -> (451, $19804)
(2021-11-08, $68997) -> (623, $68997)
사이클 하단 (저점):
(2011-11-21, $2) -> (102, $2)
(2015-08-17, $162) -> (298, $162)
(2018-12-10, $3124) -> (471, $3124)
(2022-11-21, $15473) -> (677, $15473)
해당 변곡점들은 차트 분석 좀 하시는 분들은 아시겟지만, 역대 비트코인 가격 사이클에서 가장 중요한 고점과 저점들입니다.
4. 데이터 변환
이후 선택된 값들에 10진 로그를 적용하여 기하급수적으로 증가하는 데이터를 선형 관계로 전환하였습니다. 자, 우리 어렸을 때 수학시간에 다 배운 1차 방정식인데, 기억하시죠? 다음과 같이 선형적 함수로 표현될 수 있습니다:
y = a * x + b
- x: 날짜(몇 번째 주봉인지)
- y: 비트코인 로그 값
로그를 적용한 결과, 좌표는 다음과 같이 바뀌었습니다. 이제 선형 회귀 분석이 가능한 데이터 포맷이 되었네요.
사이클 상단 (고점):
(1.903, 1.505)
(2.318, 3.093)
(2.654, 4.296)
(2.795, 4.839)
사이클 하단 (저점):
(2.009, 0.301)
(2.474, 2.210)
(2.673, 3.494)
(2.830, 4.190)
5. 선형 회귀 분석
고점과 저점 데이터를 로그 스케일로 변환한 후 각각 선형 회귀 분석을 수행하여 최적화된 추세선을 도출해봤습니다. 그 결과, 아래와 같은 방정식이 나왔네요:
사이클 상단 (고점):
y = 3.711 * x – 5.541
지수 함수로 변환하면:
가격 = 10^(3.711 * log(x)-5.541)
사이클 하단 (저점):
y = 4.782 * x – 9.385
지수 함수로 변환하면:
가격 = 10^(4.782 * log(x)-9.385)
6. 로그 채널에 편차 적용
하나의 회귀선으로 모든 변곡점을 정확히 포착하는 것은 어렵기 때문에, 육안으로 확인했을 때 주요 고점과 저점들이 어느 정도 포함될 수 있도록 적절한 범위를 설정하였습니다. 이를 위해 회귀선 위아래로 일정 비율의 편차(오프셋)을 적용하여 채널을 형성하였습니다. 예를 들어, 20%의 오프셋을 적용하면 다음과 같이 계산됩니다:
사이클 상단 = 회귀 값 * (1 + 0.2)
사이클 하단 = 회귀 값 * (1 - 0.2)
이 채널을 활용하면 비트코인의 가격이 장기 추세 대비 어느 정도 위치해 있는지 한눈에 파악할 수 있습니다. 만약 가격이 채널을 크게 벗어난다면, 시장이 과열되었거나 반대로 저평가되었을 가능성이 높다고 해석할 수 있습니다.
7. 해당 모델의 시사점
- 시장 상황 평가: 현재 비트코인 가격이 본 채널과 비교했을 때 과열(고평가) 상태인지, 혹은 침체(저평가) 상태인지 어느정도 큰 그림에서 가늠해볼 수 있습니다.
- 변곡점 예측: 가격이 채널의 상단이나 하단에 도달할 경우, 일정 수준의 조정이나 방향 전환이 나타날 가능성이 높습니다.
현재 시장 상황과 모델이 도출한 결과를 바탕으로 보면, 장기적으로 비트코인의 주요 지지 구간은 약 $24,000 ~ $36,000, 저항 구간은 $134,000 ~ $202,000 수준으로 나타납니다.
8. 결론
이번 회귀 모델을 구축하면서, 비트코인의 가격 움직임이 단순한 우연이 아니라 일정한 패턴을 따르고 있음을 다시 한번 확인할 수 있었습니다. 단순히 과거 데이터를 대입해 본 것이지만, 로그 스케일과 선형 회귀를 활용하니 꽤 직관적인 추세선이 도출되었고, 이를 통해 장기적인 흐름에서 가격이 어디쯤 위치해 있는지를 객관적으로 볼 수 있었습니다.
물론, 이 채널이 절대적인 예측 도구는 아닙니다. 시장은 항상 변수가 많고, 예상치 못한 이벤트가 터질 수도 있습니다. 하지만 적어도 큰 그림에서 시장이 과열되었는지, 혹은 저평가된 구간에 들어섰는지를 판단하는 데는 유용한 프레임워크가 될 수 있다고 생각합니다.
선형 차트로 보면, 시간이 갈수록 채널이 시사하는 지지/저항 구간의 범위가 넓어집니다. 따라서 미래로 갈수록 예측이 더욱 어려워지는 것이 이 시장의 본질일지도 모릅니다. 결국, 시장이 어느 방향으로든 극단적인 움직임을 보일 가능성은 점점 커지며, 이런 불확실성을 감안한 대응 전략이 더욱 중요하고 생가합니다.
Month of Year Performance█ OVERVIEW
The Month of Year Performance indicator is designed to visualize and compare the cumulative percentage change for each month of the year. By aggregating monthly returns, it helps uncover seasonal trends and potential anomalies in financial markets.
In financial analysis, a calendar based anomaly refers to recurring patterns or tendencies associated with specific time periods, such as days of the week. By calculating the cumulative percentage change for each month (January through December) and displaying the results both graphically and in a summary table, this indicator helps identify whether certain months
consistently outperform others.
█ FEATURES
Customisable time window via Time Settings.
Calculates cumulative percentage change for each month (January to December) separately.
Built-in error check to ensure the indicator is applied on a Monthly timeframe.
Distinct visual representation for each month using unique colours.
Customisable table settings including location and font size.
Displays a performance summary table with metrics such as performance, average return, % positive, and count.
█ HOW TO USE
Add the indicator to a chart set to a Monthly timeframe.
Select your desired Start Time and End Time in the Time Settings.
Toggle the performance table on or off in the Table Settings.
Adjust the table’s location and font size as needed.
View the cumulative monthly performance plotted in distinct colours.
Colour Scheme:
January: Blue
February: Red
March: Green
April: Orange
May: Purple
June: Fuchsia
July: Teal
August: Yellow
September: Navy
October: Lime
November: Maroon
December: Aqua
Jerusalem Session Weekday MarkingJerusalem Session Weekday Marking
This indicator applies a background color to each trading day based on the Jerusalem time zone (Asia/Jerusalem). It's a simple, no-frills tool for traders operating in markets that follow a Sunday-Thursday business week.
How It Works:
Monday to Thursday → Red background (Regular trading days)
Friday to Sunday → Green background (Weekend/non-standard trading days)
Background coloring applies to the entire day (not just session hours).
Why Use This?
Designed for traders focusing on Middle Eastern, Israeli, and Forex markets.
Helps differentiate trading days at a glance in a market where Friday-Saturday weekends are standard.
Works automatically with Jerusalem timezone, no manual adjustments needed.
🚀 Simple, effective, and useful for traders who care about local trading schedules. 🚀
Day of Week Performance█ OVERVIEW
The Day of Week Performance indicator is designed to visualise and compare the cumulative percentage change for each day of the week. This indicator explores one of the many calendar based anomalies in financial markets.
In financial analysis, a calendar based anomaly refers to recurring patterns or tendencies associated with specific time periods, such as days of the week. By calculating the cumulative percentage change for each day (Monday through Friday) and displaying the results both graphically and in a summary table, this indicator helps identify whether certain days consistently outperform others.
█ FEATURES
Customisable time window via Time Settings.
Calculates cumulative percentage change for each day (Monday to Friday) separately.
Option to use Sunday instead of Friday for CFDs and Futures analysis.
Distinct visual representation for each day using unique colours.
Customisable table settings including position and font size.
Built-in error checks to ensure the indicator is applied on a Daily timeframe.
█ HOW TO USE
Add the indicator to a chart set to a Daily timeframe.
Select your desired Start Time and End Time in the Time Settings.
Toggle the performance table on or off in the Table Settings.
Adjust the table’s location and font size as needed.
Use the "Use Sunday instead of Friday" option if your market requires it.
View the cumulative performance plotted in distinct colours.
Colour Scheme:
Monday: Blue
Tuesday: Red
Wednesday: Green
Thursday: Orange
Friday: Purple
Rajesh Raju nifty options SMA/RSIThis indicator is designed for Nifty Index Options trading on a 5-minute timeframe. It uses a combination of the Simple Moving Average (SMA) and Relative Strength Index (RSI) to identify potential buy and sell signals.
How It Works:
A BUY signal is triggered when the price crosses above the SMA and RSI is below the overbought level.
A SELL signal is generated when the price crosses below the SMA and RSI is above the oversold level.
The SMA line is displayed in pink (purple in Pine Script) to track trend direction.
Alerts are included to notify users of trade signals in real time.
This indicator helps traders make informed decisions by identifying trend reversals and momentum shifts
Vertical Lines & MACD High/Low amThis indicator tracks the MACD phase changes and identifies the highest and lowest prices within each phase. It updates the highest and lowest values for each MACD phase and displays labels directly on the candlesticks at those price points. Whenever the MACD crosses over or under the signal line, it marks the end of the previous phase and the beginning of a new one. The labels show the high and low prices as "🔺" for the peak and "🔻" for the trough. This process occurs continuously across the entire 1-minute chart.ammmmmmmmmmm
Vertical Lines & MACD High/Low pmThis indicator tracks the MACD phase changes and identifies the highest and lowest prices within each phase. It updates the highest and lowest values for each MACD phase and displays labels directly on the candlesticks at those price points. Whenever the MACD crosses over or under the signal line, it marks the end of the previous phase and the beginning of a new one. The labels show the high and low prices as "🔺" for the peak and "🔻" for the trough. This process occurs continuously across the entire 1-minute chart.
Candle Pattern ArrowsThis Pine Script code is designed to identify specific candlestick patterns and draw boxes on the chart to highlight them. Here's a detailed explanation:
1. Purpose: The script detects two specific candlestick patterns:
- A red candle followed by two green candles with no price overlap between the first and third candles.
- A green candle followed by two red candles with no price overlap between the first and third candles.
2. Conditions:
- For the first pattern, the script checks if:
- The candle two bars ago is red (close < open ).
- The next two candles are green (close > open and close > open).
- There is no price overlap between the first and third candles (high < low ).
- For the second pattern, the script checks if:
- The candle two bars ago is green (close > open ).
- The next two candles are red (close < open and close < open).
- There is no price overlap between the first and third candles (low > high ).
3. Drawing Boxes:
- When the first pattern is detected, a red box is drawn:
- The box starts from the red candle and extends 10 bars to the right.
- The height of the box spans from the highest price of the red candle (high ) to the lowest price of the third candle (low ).
- When the second pattern is detected, a green box is drawn:
- The box starts from the green candle and extends 10 bars to the right.
- The height of the box spans from the lowest price of the green candle (low ) to the highest price of the third candle (high ).
4. Box Appearance:
- The boxes have semi-transparent borders and backgrounds to ensure visibility of the underlying candles.
- The border color is set with 50% transparency, and the background color is set with 90% transparency.
5. Positioning:
- The bar_index variable is used to accurately position the boxes on the chart.
- The starting point of the box is set to the bar index of the initial candle (bar_index - 2), and it extends 10 bars to the right.
6. Customization:
- The colors and transparency levels of the boxes can be customized by adjusting the rgb and transp values in the box.new function.
7. Historical Data:
- The script works based on historical data and does not predict future patterns.
- The boxes are drawn only after the three-candle pattern is fully formed.
This script is useful for traders who want to visually identify specific candlestick patterns on their charts, helping them make more informed trading decisions.
Opening Score with DivergenceOverview
The Opening Score Indicator is a versatile tool designed to help traders assess market sentiment, trend direction, and potential reversals. By combining Opening Range Breakout (ORB), VWAP, Trend, Volatility, and Divergence Detection, this indicator provides a composite score that adapts to different market conditions.
This version includes divergence detection between the Opening Score and price, which highlights potential trend reversals or continuations before they happen. When a regular divergence occurs, the histogram bar turns orange, signaling an increased probability of a trend change.
Best for Both Intraday & Longer-Term Charts
📊 Optimized for intraday trading → Works well on 1m to 30m timeframes for short-term strategies.
📈 Also effective on longer-term charts → Can be used on 1-hour, 4-hour, daily, or weekly charts to identify macro trends and momentum shifts.
🕰️ Adapts to different market conditions → Whether you’re a day trader, swing trader, or position trader, the Opening Score helps you track trend health and reversals.
How It Works
📊 Composite Opening Score Calculation
• ORB Signal → Detects bullish/bearish breakouts based on the opening range.
• VWAP Signal → Measures price positioning relative to VWAP for trend confirmation.
• Trend Signal → Uses a moving average to determine market direction.
• Volatility Signal → Tracks ATR changes to assess market strength.
• Divergence Detection → Identifies regular and hidden divergences for potential reversals or trend continuation.
🔹 Reversal Alerts with Color-Coded Histogram
• Green Bars → Normal bullish Opening Score.
• Red Bars → Normal bearish Opening Score.
• Orange Bars → Warning! Regular Divergence detected → Possible trend reversal.
🔹 Hidden & Regular Divergence Detection
• Regular Divergence (Reversal Signals)
• 📉 Bearish Regular Divergence → Price makes a Higher High, but Opening Score makes a Lower High → 🔻 Possible Downtrend Reversal.
• 📈 Bullish Regular Divergence → Price makes a Lower Low, but Opening Score makes a Higher Low → 🔼 Possible Uptrend Reversal.
• Hidden Divergence (Trend Continuation Signals)
• 📉 Bearish Hidden Divergence → Price makes a Lower High, but Opening Score makes a Higher High → 🔻 Trend Likely to Continue Down.
• 📈 Bullish Hidden Divergence → Price makes a Higher Low, but Opening Score makes a Lower Low → 🔼 Trend Likely to Continue Up.
How to Use It
✅ Watch for Reversal Alerts (Orange Bars) → These highlight potential market turning points.
✅ Use the Zero Line as a Trend Filter → A score above 0 suggests bullish conditions, while below 0 signals bearish conditions.
✅ Combine with Market Structure & Volume Profile → Works well when paired with support/resistance levels, liquidity zones, and order flow data.
✅ Adjust settings based on timeframe → Increase moving average length & lookback periods for longer-term analysis.
Why Use This Indicator?
🚀 Works for both short-term and long-term traders → Adapts to intraday and higher timeframes.
📊 Multi-Factor Analysis → Combines multiple key market indicators for better accuracy.
🎯 Customizable Weighting → Adjust the influence of each signal to suit your trading style.
✅ No Clutter – Only the Opening Score is plotted → Keeps your chart clean & efficient.
🔔 Recommended for Intraday Trading (1m – 30m) AND Longer-Term Analysis (1H – Weekly) → Use this indicator to enhance your trend detection & reversal strategy! 🚀
