Pinescript v4 - The Holy Grail (Trailing Stop)After studying several other scripts, I believe I have found the Holy Grail! (Or perhaps I've just found a bug with Tradingview's Pinescript v4 language) Anyhow, I'm publishing this script in the hope that someone smarter than myself could shed some light on the fact that adding a trailing stop to any strategy seems to make it miraculously...no that's an understatement...incredulously, stupendously, mind-bendingly profitable. I'm talking about INSANE profit factors, higher than 200x, with drawdowns of <10%. Sounds too good to be true? Maybe it is...or you could hook it up to your LIVE broker, and pray it doesn't explode. This is an upgraded version of my original Pin Bar Strategy.
Recommended Chart Settings:
Asset Class: Forex
Time Frame: H1
Long Entry Conditions:
a) Exponential Moving Average Fan up trend
b) Presence of a Bullish Pin Bar
c) Pin Bar pierces the Exponential Moving Average Fan
Short Entry Conditions:
a) Exponential Moving Average down trend
b) Presence of a Bearish Pin Bar
c) Pin Bar pierces the Exponential Moving Average Fan
Exit Conditions:
a) Trailing stop is hit
b) Moving Averages cross-back (optional)
c) It's the weekend
Default Robot Settings:
Equity Risk (%): 3 //how much account balance to risk per trade
Stop Loss (x*ATR, Float): 0.5 //stoploss = x * ATR, you can change x
Stop Loss Trail Points (Pips): 1 //the magic sauce, not sure how this works
Stop Loss Trail Offset (Pips): 1 //the magic sauce, not sure how this works
Slow SMA (Period): 50 //slow moving average period
Medium EMA (Period): 18 //medium exponential moving average period
Fast EMA (Period): 6 //fast exponential moving average period
ATR (Period): 14 // average true range period
Cancel Entry After X Bars (Period): 3 //cancel the order after x bars not triggered, you can change x
Backtest Results (2019 to 2020, H1, Default Settings):
AUDUSD - 1604% profit, 239.6 profit factor, 4.9% drawdown (INSANE)
NZDUSD - 1688.7% profit, 100.3 profit factor, 2.5% drawdown
GBPUSD - 1168.8% profit, 98.7 profit factor, 0% drawdown
USDJPY - 900.7% profit, 93.7 profit factor, 4.9% drawdown
USDCAD - 819% profit, 31.7 profit factor, 8.1% drawdown
EURUSD - 685.6% profit, 26.8 profit factor, 5.9% drawdown
USDCHF - 1008% profit, 18.7 profit factor, 8.6% drawdown
GBPJPY - 1173.4% profit, 16.1 profit factor, 7.9% drawdown
EURAUD - 613.3% profit, 14.4 profit factor, 9.8% drawdown
AUDJPY - 1619% profit, 11.26 profit factor, 9.1% drawdown
EURJPY - 897.2% profit, 6 profit factor, 13.8% drawdown
EURGBP - 608.9% profit, 5.3 profit factor, 9.8% drawdown (NOT TOO SHABBY)
As you can clearly see above, this forex robot is projected by the Tradingview backtester to be INSANELY profitable for all common forex pairs. So what was the difference between this strategy and my previous strategies? Check my code and look for "trail_points" and "trail_offset"; you can even look them up in the PineScript v4 documentation. They specify a trailing stop as the exit condition, which automatically closes the trade if price reverses against you.
I however suspect that the backtester is not properly calculating intra-bar price movement, and is using a simplified model. With this simplfied approach, the trailing stop code becomes some sort of "holy grail" generator, making every trade entered profitable.
Risk Warning:
This is a forex trading strategy that involves high risk of equity loss, and backtest performance will not equal future results. You agree to use this script at your own risk.
Hint:
To get more realistic results, and *maybe* overcome the intrabar simulation error, change the settings to: "Stop Loss Trail Points (pips)": 100
I am not sure if this eradicates the bug, but the entries and exits look more proper, and the profit factors are more believable.
Cari skrip untuk "backtest"
Forward testing code block by CanundoThis is purely a code block useable for strategies you want to forward test. No backtest will happen.
It's especially important to figure out if a strategy will perform as well in live trading as it does in backtesting since the trades in backtests are calculated differently. It can be a great difference.
It's best to test it on 1 minute timeframe or lower. It would take 1-2 minutes to see the first trade happen while no trade before exists.
Dual Chain StrategyDual Chain Strategy - Technical Overview
How It Works:
The Dual Chain Strategy is a unique approach to trading that utilizes Exponential Moving Averages (EMAs) across different timeframes, creating two distinct "chains" of trading signals. These chains can work independently or together, capturing both long-term trends and short-term price movements.
Chain 1 (Longer-Term Focus):
Entry Signal: The entry signal for Chain 1 is generated when the closing price crosses above the EMA calculated on a weekly timeframe. This suggests the start of a bullish trend and prompts a long position.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Exit Signal: The exit signal is triggered when the closing price crosses below the EMA on a daily timeframe, indicating a potential bearish reversal.
exitLongChain1 = enableChain1 and ta.crossunder(src1, exitEMA1)
Parameters: Chain 1's EMA length is set to 10 periods by default, with the flexibility for user adjustment to match various trading scenarios.
Chain 2 (Shorter-Term Focus):
Entry Signal: Chain 2 generates an entry signal when the closing price crosses above the EMA on a 12-hour timeframe. This setup is designed to capture quicker, shorter-term movements.
bullishChain2 = enableChain2 and ta.crossover(src2, entryEMA2)
Exit Signal: The exit signal occurs when the closing price falls below the EMA on a 9-hour timeframe, indicating the end of the shorter-term trend.
exitLongChain2 = enableChain2 and ta.crossunder(src2, exitEMA2)
Parameters: Chain 2's EMA length is set to 9 periods by default, and can be customized to better align with specific market conditions or trading strategies.
Key Features:
Dual EMA Chains: The strategy's originality shines through its dual-chain configuration, allowing traders to monitor and react to both long-term and short-term market trends. This approach is particularly powerful as it combines the strengths of trend-following with the agility of momentum trading.
Timeframe Flexibility: Users can modify the timeframes for both chains, ensuring the strategy can be tailored to different market conditions and individual trading styles. This flexibility makes it versatile for various assets and trading environments.
Independent Trade Logic: Each chain operates independently, with its own set of entry and exit rules. This allows for simultaneous or separate execution of trades based on the signals from either or both chains, providing a robust trading system that can handle different market phases.
Backtesting Period: The strategy includes a configurable backtesting period, enabling thorough performance assessment over a historical range. This feature is crucial for understanding how the strategy would have performed under different market conditions.
time_cond = time >= startDate and time <= finishDate
What It Does:
The Dual Chain Strategy offers traders a distinctive trading tool that merges two separate EMA-based systems into one cohesive framework. By integrating both long-term and short-term perspectives, the strategy enhances the ability to adapt to changing market conditions. The originality of this script lies in its innovative dual-chain design, providing traders with a unique edge by allowing them to capitalize on both significant trends and smaller, faster price movements.
Whether you aim to capture extended market trends or take advantage of more immediate price action, the Dual Chain Strategy provides a comprehensive solution with a high degree of customization and strategic depth. Its flexibility and originality make it a valuable tool for traders seeking to refine their approach to market analysis and execution.
How to Use the Dual Chain Strategy
Step 1: Access the Strategy
Add the Script: Start by adding the Dual Chain Strategy to your TradingView chart. You can do this by searching for the script by name or using the link provided.
Select the Asset: Apply the strategy to your preferred trading pair or asset, such as #BTCUSD, to see how it performs.
Step 2: Configure the Settings
Enable/Disable Chains:
The strategy is designed with two independent chains. You can choose to enable or disable each chain depending on your trading style and the market conditions.
enableChain1 = input.bool(true, title='Enable Chain 1')
enableChain2 = input.bool(true, title='Enable Chain 2')
By default, both chains are enabled. If you prefer to focus only on longer-term trends, you might disable Chain 2, or vice versa if you prefer shorter-term trades.
Set EMA Lengths:
Adjust the EMA lengths for each chain to match your trading preferences.
Chain 1: The default EMA length is 10 periods. This chain uses a weekly timeframe for entry signals and a daily timeframe for exits.
len1 = input.int(10, minval=1, title='Length Chain 1 EMA', group="Chain 1")
Chain 2: The default EMA length is 9 periods. This chain uses a 12-hour timeframe for entries and a 9-hour timeframe for exits.
len2 = input.int(9, minval=1, title='Length Chain 2 EMA', group="Chain 2")
Customize Timeframes:
You can customize the timeframes used for entry and exit signals for both chains.
Chain 1:
Entry Timeframe: Weekly
Exit Timeframe: Daily
tf1_entry = input.timeframe("W", title='Chain 1 Entry Timeframe', group="Chain 1")
tf1_exit = input.timeframe("D", title='Chain 1 Exit Timeframe', group="Chain 1")
Chain 2:
Entry Timeframe: 12 Hours
Exit Timeframe: 9 Hours
tf2_entry = input.timeframe("720", title='Chain 2 Entry Timeframe (12H)', group="Chain 2")
tf2_exit = input.timeframe("540", title='Chain 2 Exit Timeframe (9H)', group="Chain 2")
Set the Backtesting Period:
Define the period over which you want to backtest the strategy. This allows you to see how the strategy would have performed historically.
startDate = input.time(timestamp('2015-07-27'), title="StartDate")
finishDate = input.time(timestamp('2026-01-01'), title="FinishDate")
Step 3: Analyze the Signals
Understand the Entry and Exit Signals:
Buy Signals: When the price crosses above the entry EMA, the strategy generates a buy signal.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Sell Signals: When the price crosses below the exit EMA, the strategy generates a sell signal.
bearishChain2 = enableChain2 and ta.crossunder(src2, entryEMA2)
Review the Visual Indicators:
The strategy plots buy and sell signals on the chart with labels for easy identification:
BUY C1/C2 for buy signals from Chain 1 and Chain 2.
SELL C1/C2 for sell signals from Chain 1 and Chain 2.
This visual aid helps you quickly understand when and why trades are being executed.
Step 4: Optimize the Strategy
Backtest Results:
Review the strategy’s performance over the backtesting period. Look at key metrics like net profit, drawdown, and trade statistics to evaluate its effectiveness.
Adjust the EMA lengths, timeframes, and other settings to see how changes affect the strategy’s performance.
Customize for Live Trading:
Once satisfied with the backtest results, you can apply the strategy settings to live trading. Remember to continuously monitor and adjust as needed based on market conditions.
Step 5: Implement Risk Management
Use Realistic Position Sizing:
Keep your risk exposure per trade within a comfortable range, typically between 1-2% of your trading capital.
Set Alerts:
Set up alerts for buy and sell signals, so you don’t miss trading opportunities.
Paper Trade First:
Consider running the strategy in a paper trading account to understand its behavior in real market conditions before committing real capital.
This dual-layered approach offers a distinct advantage: it enables the strategy to adapt to varying market conditions by capturing both broad trends and immediate price action without one chain's activity impacting the other's decision-making process. The independence of these chains in executing transactions adds a level of sophistication and flexibility that is rarely seen in more conventional trading systems, making the Dual Chain Strategy not just unique, but a powerful tool for traders seeking to navigate complex market environments.
Long-Only Opening Range Breakout (ORB) with Pivot PointsIntraday Trading Strategy: Long-Only Opening Range Breakout (ORB) with Pivot Points
Background:
Opening Range Breakout (ORB) is a popular long-only trading strategy that capitalizes on the early morning volatility in financial markets. It's based on the idea that the initial price movements during the first few minutes or hours of the trading day can set the tone for the rest of the session. The strategy involves identifying a price range within which the asset trades during the opening period and then taking long positions when the price breaks out to the upside of this range.
Pivot Points are a widely used technical indicator in trading. They represent potential support and resistance levels based on the previous day's price action. Pivot points are calculated using the previous day's high, low, and close prices and can help traders identify key price levels for making trading decisions.
How to Use the Script:
Initialization: This script is written in Pine Script, a domain-specific language for trading strategies on the TradingView platform. To use this script, you need to have access to TradingView.
Apply the Script: You can do this by adding it to your favorites, then selecting the script in the indicators list under favorites or by searching for it by name under community scripts.
Customize Settings: The script allows you to customize various settings through the TradingView interface. These settings include:
Opening Session: You can set the time frame for the opening session.
Max Trades per Day: Specify the maximum number of long trades allowed per trading day.
Initial Stop Loss Type: Choose between using a percentage-based stop loss or the previous candles low for stop loss calculations.
Stop Loss Percentage: If you select the percentage-based stop loss, specify the percentage of the entry price for the stop loss.
Backtesting Start and End Time: Set the time frame for backtesting the strategy.
Strategy Signals:
The script will display pivot points in blue (R1, R2, R3, R4, R5) and half-pivot points in gray (R0.5, R1.5, R2.5, R3.5, R4.5) on your chart.
The green line represents the opening range.
The script generates long (buy) signals based on specific conditions:
---The open price is below the opening range high (h).
---The current high price is above the opening range high.
---Pivot point R1 is above the opening range high.
---It's a long-only strategy designed to capture upside breakouts.
---It also respects the maximum number of long trades per day.
The script manages long positions, calculates stop losses, and adjusts long positions according to the defined rules.
Trailing Stop Mechanism
The script incorporates a dynamic trailing stop mechanism designed to protect and maximize profits for long positions. Here's how it works:
1. Initialization:
The script allows you to choose between two types of initial stop loss:
---Percentage-based: This option sets the initial stop loss as a percentage of the entry price.
---Previous day's low: This option sets the initial stop loss at the previous day's low.
2. Setting the Initial Stop Loss (`sl_long0`):
The initial stop loss (`sl_long0`) is calculated based on the chosen method:
---If "Percentage" is selected, it calculates the stop loss as a percentage of the entry price.
---If "Previous Low" is selected, it sets the stop loss at the previous day's low.
3. Dynamic Trailing Stop (`trail_long`):
The script then monitors price movements and uses a dynamic trailing stop mechanism (`trail_long`) to adjust the stop loss level for long positions.
If the current high price rises above certain pivot point levels, the trailing stop is adjusted upwards to lock in profits.
The trailing stop levels are calculated based on pivot points (`r1`, `r2`, `r3`, etc.) and half-pivot points (`r0.5`, `r1.5`, `r2.5`, etc.).
The script checks if the high price surpasses these levels and, if so, updates the trailing stop accordingly.
This dynamic trailing stop allows traders to secure profits while giving the position room to potentially capture additional gains.
4. Final Stop Loss (`sl_long`):
The script calculates the final stop loss level (`sl_long`) based on the following logic:
---If no position is open (`pos == 0`), the stop loss is set to zero, indicating there is no active stop loss.
---If a position is open (`pos == 1`), the script calculates the maximum of the initial stop loss (`sl_long0`) and the dynamic trailing stop (`trail_long`).
---This ensures that the stop loss is always set to the more conservative of the two values to protect profits.
5. Plotting the Stop Loss:
The script plots the stop loss level on the chart using the `plot` function.
It will only display the stop loss level if there is an open position (`pos == 1`) and it's not a new trading day (`not newday`).
The stop loss level is shown in red on the chart.
By combining an initial stop loss with a dynamic trailing stop based on pivot points and half-pivot points, the script aims to provide a comprehensive risk management mechanism for long positions. This allows traders to lock in profits as the price moves in their favor while maintaining a safeguard against adverse price movements.
End of Day (EOD) Exit:
The script includes an "End of Day" (EOD) exit mechanism to automatically close any open positions at the end of the trading day. This feature is designed to manage and control positions when the trading day comes to a close. Here's how it works:
1. Initialization:
At the beginning of each trading day, the script identifies a new trading day using the `is_newbar('D')` condition.
When a new trading day begins, the `newday` variable becomes `true`, indicating the start of a new trading session.
2. Plotting the "End of Day" Signal:
The script includes a plot on the chart to visually represent the "End of Day" signal. This is done using the `plot` function.
The plot is labeled "DayEnd" and is displayed as a comment on the chart. It signifies the EOD point.
3. EOD Exit Condition:
When the script detects that a new trading day has started (`newday == true`), it triggers the EOD exit condition.
At this point, the script proceeds to close all open positions that may have been active during the trading day.
4. Closing Open Positions:
The `strategy.close_all` function is used to close all open positions when the EOD exit condition is met.
This function ensures that any remaining long positions are exited, regardless of their current profit or loss.
The function also includes an `alert_message`, which can be customized to send an alert or notification when positions are closed at EOD.
Purpose of EOD Exit
The "End of Day" exit mechanism serves several essential purposes in the trading strategy:
Risk Management: It helps manage risk by ensuring that positions are not left open overnight when markets can experience increased volatility.
Capital Preservation: Closing positions at EOD can help preserve trading capital by avoiding potential adverse overnight price movements.
Rule-Based Exit: The EOD exit is rule-based and automatic, ensuring that it is consistently applied without emotions or manual intervention.
Scalability: It allows the strategy to be applied to various markets and timeframes where EOD exits may be appropriate.
By incorporating an EOD exit mechanism, the script provides a comprehensive approach to managing positions, taking profits, and minimizing risk as each trading day concludes. This can be especially important in volatile markets like cryptocurrencies, where overnight price swings can be significant.
Backtesting: The script includes a backtesting feature that allows you to test the strategy's performance over historical data. Set the start and end times for backtesting to see how the long-only strategy would have performed in the past.
Trade Execution: If you choose to use this script for live trading, make sure you understand the risks involved. It's essential to set up proper risk management, including position sizing and stop loss orders.
Monitoring: Monitor the long-only strategy's performance over time and be prepared to make adjustments as market conditions change.
Disclaimer: Trading carries a risk of capital loss. This script is provided for educational purposes and as a starting point for your own long-only strategy development. Always do your own research and consider seeking advice from a qualified financial professional before making trading decisions.
MLExtensionsLibrary "MLExtensions"
normalizeDeriv(src, quadraticMeanLength)
Returns the smoothed hyperbolic tangent of the input series.
Parameters:
src : The input series (i.e., the first-order derivative for price).
quadraticMeanLength : The length of the quadratic mean (RMS).
Returns: nDeriv The normalized derivative of the input series.
normalize(src, min, max)
Rescales a source value with an unbounded range to a target range.
Parameters:
src : The input series
min : The minimum value of the unbounded range
max : The maximum value of the unbounded range
Returns: The normalized series
rescale(src, oldMin, oldMax, newMin, newMax)
Rescales a source value with a bounded range to anther bounded range
Parameters:
src : The input series
oldMin : The minimum value of the range to rescale from
oldMax : The maximum value of the range to rescale from
newMin : The minimum value of the range to rescale to
newMax : The maximum value of the range to rescale to
Returns: The rescaled series
color_green(prediction)
Assigns varying shades of the color green based on the KNN classification
Parameters:
prediction : Value (int|float) of the prediction
Returns: color
color_red(prediction)
Assigns varying shades of the color red based on the KNN classification
Parameters:
prediction : Value of the prediction
Returns: color
tanh(src)
Returns the the hyperbolic tangent of the input series. The sigmoid-like hyperbolic tangent function is used to compress the input to a value between -1 and 1.
Parameters:
src : The input series (i.e., the normalized derivative).
Returns: tanh The hyperbolic tangent of the input series.
dualPoleFilter(src, lookback)
Returns the smoothed hyperbolic tangent of the input series.
Parameters:
src : The input series (i.e., the hyperbolic tangent).
lookback : The lookback window for the smoothing.
Returns: filter The smoothed hyperbolic tangent of the input series.
tanhTransform(src, smoothingFrequency, quadraticMeanLength)
Returns the tanh transform of the input series.
Parameters:
src : The input series (i.e., the result of the tanh calculation).
smoothingFrequency
quadraticMeanLength
Returns: signal The smoothed hyperbolic tangent transform of the input series.
n_rsi(src, n1, n2)
Returns the normalized RSI ideal for use in ML algorithms.
Parameters:
src : The input series (i.e., the result of the RSI calculation).
n1 : The length of the RSI.
n2 : The smoothing length of the RSI.
Returns: signal The normalized RSI.
n_cci(src, n1, n2)
Returns the normalized CCI ideal for use in ML algorithms.
Parameters:
src : The input series (i.e., the result of the CCI calculation).
n1 : The length of the CCI.
n2 : The smoothing length of the CCI.
Returns: signal The normalized CCI.
n_wt(src, n1, n2)
Returns the normalized WaveTrend Classic series ideal for use in ML algorithms.
Parameters:
src : The input series (i.e., the result of the WaveTrend Classic calculation).
n1
n2
Returns: signal The normalized WaveTrend Classic series.
n_adx(highSrc, lowSrc, closeSrc, n1)
Returns the normalized ADX ideal for use in ML algorithms.
Parameters:
highSrc : The input series for the high price.
lowSrc : The input series for the low price.
closeSrc : The input series for the close price.
n1 : The length of the ADX.
regime_filter(src, threshold, useRegimeFilter)
Parameters:
src
threshold
useRegimeFilter
filter_adx(src, length, adxThreshold, useAdxFilter)
filter_adx
Parameters:
src : The source series.
length : The length of the ADX.
adxThreshold : The ADX threshold.
useAdxFilter : Whether to use the ADX filter.
Returns: The ADX.
filter_volatility(minLength, maxLength, useVolatilityFilter)
filter_volatility
Parameters:
minLength : The minimum length of the ATR.
maxLength : The maximum length of the ATR.
useVolatilityFilter : Whether to use the volatility filter.
Returns: Boolean indicating whether or not to let the signal pass through the filter.
backtest(high, low, open, startLongTrade, endLongTrade, startShortTrade, endShortTrade, isStopLossHit, maxBarsBackIndex, thisBarIndex)
Performs a basic backtest using the specified parameters and conditions.
Parameters:
high : The input series for the high price.
low : The input series for the low price.
open : The input series for the open price.
startLongTrade : The series of conditions that indicate the start of a long trade.`
endLongTrade : The series of conditions that indicate the end of a long trade.
startShortTrade : The series of conditions that indicate the start of a short trade.
endShortTrade : The series of conditions that indicate the end of a short trade.
isStopLossHit : The stop loss hit indicator.
maxBarsBackIndex : The maximum number of bars to go back in the backtest.
thisBarIndex : The current bar index.
Returns: A tuple containing backtest values
init_table()
init_table()
Returns: tbl The backtest results.
update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winrate, stopLosses)
update_table(tbl, tradeStats)
Parameters:
tbl : The backtest results table.
tradeStatsHeader : The trade stats header.
totalTrades : The total number of trades.
totalWins : The total number of wins.
totalLosses : The total number of losses.
winLossRatio : The win loss ratio.
winrate : The winrate.
stopLosses : The total number of stop losses.
Returns: Updated backtest results table.
Squeeze Momentum Strategy [LazyBear] Buy Sell TP SL Alerts-Modified version of Squeeze Momentum Indicator by @LazyBear.
-Converted to version 5,
-Taken inspiration from @KivancOzbilgic for its buy sell calculations,
-Used @Bunghole strategy template with Take Profit, Stop Loss and Enable/Disable Toggles
-Added Custom Date Backtesting Module
------------------------------------------------------------------------------------------------------------------------
All credit goes to above
Problem with original version:
The original Squeeze Momentum Strategy did not have buy sell signals and there was alot of confusion as to when to enter and exit.
There was no proper strategy that would allow backtesting on which further analysis could be carried out.
There are 3 aspects this strategy:
1 ) Strategy Logic (easily toggleable from the dropdown menu from strategy settings)
- LazyBear (I have made this simple by using Kivanc technique of Momentums Moving Average Crossover, BUY when MA cross above signal line, SELL when crossdown signal line)
- Zero Crossover Line (BUY signal when crossover zero line, and SELL crossdown zero line)
2) Long Short TP and SL
- In strategies there is usually only 1 SL and 1 TP, and it is assumed that if a 2% SL giving a good profit %, then it would be best for both long and short. However this is not the case for many. Many markets/pairs, go down with much more speed then they go up with. Hence once we have a profitable backtesting setting, then we should start optimizing Long and Short SL's seperately. Once that is done, we should start optimizing for Long and Short TP's separately, starting with Longs first in both cases.
3) Enable and Disable Toggles of Long and Short Trades
- Many markets dont allow short trades, or are not suitable for short trades. In this case it would be much more feasible to disable "Short" Trading and see results of Long Only as a built in graphic view of backtestor provides a more easy to understand data feed as compared to the performance summary in which you have to review long and short profitability separately.
4) Custom Data Backtesting
- One of most crucial aspects while optimizing for backtesting is to check a strategies performance on uptrends, downtrend and sideways markets seperately as to understand the weak points of strategy.
- Once you enable custom date backtesting, you will see lines on the chart which can be dragged left right based on where you want to start and end the backtesting from and to.
Note:
- Not a financial advise
- Open to feedback, questions, improvements, errors etc.
- More info on how the squeeze momentum works visit LazyBear indicator link:
Happy Trading!
Cheers
M Tahreem Alam @mtahreemalam
ICT HTF Volume Candles (Based on HTF Candles by Fadi)# ICT HTF Volume Candles - Multi-Timeframe Volume Analysis
## Overview
This indicator provides multi-timeframe volume visualization designed to complement price action analysis. It displays volume data from up to 6 higher timeframes simultaneously in a separate panel, allowing traders to identify volume spikes, divergences, and institutional activity without switching between timeframes.
**Original Concept Credits:** This indicator builds upon the HTF Candles framework by Fadi, adapting it specifically for volume analysis with enhanced features including gap-filling for extended hours, multiple scaling methods, and advanced synchronization.
## What Makes This Script Original
### Key Innovations:
1. **Three Volume Scaling Methods:**
- **Per-HTF Auto Scale:** Each timeframe scales independently for detailed comparison
- **Global Auto Scale:** All timeframes use unified scale for relative volume comparison
- **Manual Scale:** User-defined maximum for consistent analysis across sessions
2. **Bullish/Bearish Volume Differentiation:**
- Volume bars colored based on price movement (close vs open)
- Separate styling for bullish (green) and bearish (red) volume periods
- Helps identify whether volume supports price direction
3. **Advanced Time Synchronization:**
- Custom daily candle open times (Midnight, 8:30 AM, 9:30 AM ET)
- Timezone-aware calculations for New York trading hours
- Real-time countdown timers for each timeframe
- **Gap-filling technology** for continuous display during extended hours and weekends
4. **Flexible Display Options:**
- Configurable spacing and positioning
- Label placement (top, bottom, or both)
- Day-of-week or time interval labels on candles
- Works reliably in backtesting and live trading
## How It Works
### Volume Calculation
The indicator uses `request.security()` with optimized parameters to fetch volume data from higher timeframes:
- **Volume Open/High/Low/Close (OHLC):** Tracks volume changes within each HTF candle
- **Color Logic:** Compares HTF close vs open prices to determine bullish/bearish classification
- **Alignment:** All volume bars share a common baseline for easy visual comparison
- **Gap Handling:** Uses `gaps=barmerge.gaps_off` to maintain continuity during non-trading hours
### Technical Implementation
```
1. Monitors HTF timeframe changes using request.security() with lookahead
2. Creates new VolumeCandle object when HTF bar opens
3. Updates current candle's volume H/L/C on each chart bar
4. Applies selected scaling method to normalize display height
5. Repositions all candles and labels on each bar update
6. Fills gaps automatically during extended hours for consistent display
```
### Scaling Methods Explained
**Method 1 - Auto Scale per HTF:**
Each timeframe displays volume relative to its own maximum. Best for identifying patterns within each individual timeframe.
**Method 2 - Global Auto Scale:**
All timeframes share the same scale based on the highest volume across all HTFs. Best for comparing relative volume strength between timeframes.
**Method 3 - Manual Scale:**
User sets maximum volume value. Best for maintaining consistent scale across different trading sessions or instruments.
## How to Use This Indicator
### Setup
1. Add indicator to your chart (it appears in a separate panel below price)
2. Configure up to 6 higher timeframes (default: 5m, 15m, 1H, 4H, 1D, 1W)
3. Set number of candles to display for each timeframe
4. Choose volume scaling method based on your analysis needs
5. Enable "Fix gaps in non-trading hours" for extended hours trading (enabled by default)
### Interpretation
**Volume Spikes:**
- Sudden increase in volume height indicates institutional activity or strong conviction
- Compare volume between timeframes to identify where the real money is moving
- Look for volume spikes that appear across multiple timeframes simultaneously
**Bullish vs Bearish Volume:**
- **Green volume bars:** Price closed higher (buying pressure)
- **Red volume bars:** Price closed lower (selling pressure)
- High green volume during uptrend = confirmation of strength
- High red volume during downtrend = confirmation of weakness
- High volume opposite to trend = potential reversal warning
**Multi-Timeframe Context:**
- **5m/15m:** Scalping and day trading activity
- **1H/4H:** Swing trading and intraday institutional flows
- **Daily/Weekly:** Major position building and long-term trends
**Divergences:**
- Price making new highs but volume declining = weakening trend
- Volume increasing while price consolidates = potential breakout brewing
- Price breaks level but volume doesn't confirm = likely false breakout
### Practical Examples
**Example 1 - Institutional Confirmation:**
Price breaks above resistance. Check volume across timeframes:
- 5m shows spike = retail interest
- 15m + 1H + 4H all show spikes = institutional confirmation
- **Trade confidence: HIGH**
**Example 2 - False Breakout Detection:**
Price breaks resistance with:
- High volume on 5m only
- Normal/low volume on 1H and 4H
- **Interpretation:** Likely retail trap, institutions not participating
- **Action:** Wait for pullback or avoid
**Example 3 - Accumulation Phase:**
Price ranges sideways but:
- Daily volume gradually increasing
- Weekly volume above average
- **Interpretation:** Smart money accumulating
- **Action:** Prepare for breakout in direction of volume
**Example 4 - Volume Divergence:**
Price makes new high:
- Current high has lower volume than previous high across all timeframes
- **Interpretation:** Weakening momentum
- **Action:** Consider profit-taking or reversal trade
## Configuration Parameters
### Timeframe Settings
- **HTF 1-6:** Select timeframes (must be higher than chart timeframe)
- **Max Display:** Number of candles to show per timeframe (1-50)
- **Limit to Next HTFs:** Display only first N enabled timeframes (1-6)
### Styling
- **Bull/Bear Colors:** Separate colors for body, border, and wick
- **Padding from current candles:** Distance offset from live price action
- **Space between candles:** Gap between individual volume bars
- **Space between Higher Timeframes:** Gap between different timeframe groups
- **Candle Width:** Thickness of volume bars (1-4, multiplied by 2)
### Volume Settings
- **Volume Scale Method:** Choose 1, 2, or 3
- 1 = Auto Scale per HTF (each TF independent)
- 2 = Global Auto Scale (all TF unified)
- 3 = Manual Scale (user-defined max)
- **Auto Scale Volume:** Enable/disable automatic scaling
- **Manual Scale Max Volume:** Set maximum when using Method 3
### Label Settings
- **HTF Label:** Show/hide timeframe names with color and size options
- **Label Positions:** Display at Top, Bottom, or Both
- **Label Alignment:** Align centered or Follow Candles
- **Remaining Time:** Show countdown timer until next HTF candle
- **Interval Value:** Display day-of-week or time on each candle
### Custom Daily Candle
- **Enable Custom Daily:** Override default daily candle timing
- **Open Time Options:**
- **Midnight:** Standard 00:00 ET daily open
- **8:30 AM:** Align with economic data releases
- **9:30 AM:** Align with NYSE market open
- Useful for specific trading strategies or market alignment
### Advanced Settings
- **Fix gaps in non-trading hours:** Maintains alignment during extended hours and weekends (recommended: ON)
- Prevents visual gaps during forex weekend closures
- Ensures consistent display during crypto 24/7 trading
- Improves backtesting reliability
## Best Practices
1. **Pair with Price Action:** Use alongside HTF price candles indicator for complete picture
2. **Start Simple:** Enable 2-3 timeframes initially (e.g., 15m, 1H, 4H), add more as needed
3. **Match Settings:** Use same candle width/spacing as companion price indicator for visual alignment
4. **Scale Appropriately:**
- Use **Global scale** (Method 2) when comparing timeframes
- Use **Per-HTF scale** (Method 1) for pattern analysis within each timeframe
- Use **Manual scale** (Method 3) for consistent day-to-day comparison
5. **Watch for Volume Clusters:** High volume appearing simultaneously across multiple HTFs signals significant market events
6. **Confirm Breakouts:** Always check if volume supports the price movement across higher timeframes
7. **Extended Hours:** Keep "Fix gaps" enabled for 24/7 markets (Forex, Crypto) and weekend analysis
## Technical Notes
- **Timezone:** All calculations use America/New_York timezone for consistency
- **Real-time Updates:** Volume and timers update on each tick during market hours
- **Performance:** Optimized with max_bars_back=5000 for extensive historical analysis
- **Compatibility:** Works on all instruments with volume data (Stocks, Forex, Crypto, Futures)
- **Gap Handling:** Uses `barmerge.gaps_off` to fill data gaps during non-trading periods
- **Backtesting:** Uses `lookahead=barmerge.lookahead_on` for stable historical data without repainting
- **Data Continuity:** Automatically handles market closures, weekends, and extended hours
## Updates & Improvements
**Version 2.0 (Current):**
- ✅ Fixed alignment issues during extended hours and weekends
- ✅ Eliminated repainting in backtesting
- ✅ Added gap-filling technology for continuous display
- ✅ Improved data synchronization across all timeframes
- ✅ Enhanced NA value handling for data integrity
- ✅ Added advanced settings group for user control
## Support
For questions, suggestions, or feedback, please comment on the publication or message the author.
---
**Disclaimer:** This indicator is for educational and informational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always perform your own analysis and implement proper risk management before making trading decisions.
RMSD Trend [InvestorUnknown]RMSD Trend is a trend-following indicator that utilizes Root Mean Square Deviation (RMSD) to dynamically construct a volatility-weighted trend channel around a selected moving average. This indicator is designed to enhance signal clarity, minimize noise, and offer quantitative insights into market momentum, ideal for both discretionary and systematic traders.
How It Works
At its core, RMSD Trend calculates a deviation band around a selected moving average using the Root Mean Square Deviation (similar to standard deviation but with squared errors), capturing the magnitude of price dispersion over a user-defined period. The logic is simple:
When price crosses above the upper deviation band, the market is considered bullish (Risk-ON Long).
When price crosses below the lower deviation band, the market is considered bearish (Risk-ON Short).
If price stays within the band, the market is interpreted as neutral or ranging, offering low-risk decision zones.
The indicator also generates trend flips (Long/Short) based on crossovers and crossunders of the price and the RMSD bands, and colors candles accordingly for enhanced visual feedback.
Features
7 Moving Average Types: Choose between SMA, EMA, HMA, DEMA, TEMA, RMA, and FRAMA for flexibility.
Customizable Source Input: Use price types like close, hl2, ohlc4, etc.
Volatility-Aware Channel: Adjustable RMSD multiplier determines band width based on volatility.
Smart Coloring: Candles and bands adapt their colors to reflect trend direction (green for bullish, red for bearish).
Intra-bar Repainting Toggle: Option to allow more responsive but repaintable signals.
Speculation Fill Zones: When price exceeds the deviation channel, a semi-transparent fill highlights potential momentum surges.
Backtest Mode
Switching to Backtest Mode unlocks a robust suite of simulation features:
Built-in Equity Curve: Visualizes both strategy equity and Buy & Hold performance.
Trade Metrics Table: Displays the number of trades, win rates, gross profits/losses, and long/short breakdowns.
Performance Metrics Table: Includes key stats like CAGR, drawdown, Sharpe ratio, and more.
Custom Date Range: Set a custom start date for your backtest.
Trade Sizing: Simulate results using position sizing and initial capital settings.
Signal Filters: Choose between Long & Short, Long Only, or Short Only strategies.
Alerts
The RMSD Trend includes six built-in alert conditions:
LONG (RMSD Trend) - Trend flips from Short to Long
SHORT (RMSD Trend) - Trend flips from Long to Short
RISK-ON LONG (RMSD Trend) - Price crosses above upper RMSD band
RISK-OFF LONG (RMSD Trend) - Price falls back below upper RMSD band
RISK-ON SHORT (RMSD Trend) - Price crosses below lower RMSD band
RISK-OFF SHORT (RMSD Trend) - Price rises back above lower RMSD band
Use Cases
Trend Confirmation: Confirms directional bias with RMSD-weighted confidence zones.
Breakout Detection: Highlights moments when price breaks free from historical volatility norms.
Mean Reversion Filtering: Avoids false signals by incorporating RMSD’s volatility sensitivity.
Strategy Development: Backtest your signals or integrate with a broader system for alpha generation.
Settings Summary
Display Mode: Overlay (default) or Backtest Mode
Average Type: Choose from SMA, EMA, HMA, DEMA, etc.
Average Length: Lookback window for moving average
RMSD Multiplier: Band width control based on RMS deviation
Source: Input price source (close, hl2, ohlc4, etc.)
Intra-bar Updating: Real-time updates (may repaint)
Color Bars: Toggle bar coloring by trend direction
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Past performance, including backtest results, is not indicative of future results. Use with caution and always test thoroughly before live deployment.
Alans Date Range CalculatorOverview
Setting a date range for backtesting enables you to evaluate your trading strategy under various market conditions. Traders can test a strategy’s performance during specific periods, such as economic downturns, bull markets, or periods of high volatility. This helps assess the trading strategy’s robustness and adaptability across different scenarios.
Specifying years of data instead of just inputting specific start and end dates offers several advantages:
1. **Consistency**: Using a fixed number of years ensures that the testing period is consistent across different strategies or iterations. This makes it easier to compare performance metrics and draw meaningful conclusions.
2. **Flexibility**: Specifying years allows for automatic adjustment of the start date based on the current date or selected end date. This is particularly useful when new data becomes available or when testing on different assets with varying historical data lengths.
3. **Efficiency**: It simplifies updating and retesting strategies. Instead of recalculating specific start dates each time, traders can quickly adjust the number of years to process, making it easier to test strategies over different timeframes.
4. **Comprehensive Analysis**: Broader timeframes defined by years help you evaluate how your strategy performs over multiple market cycles, providing insights into long-term viability and potential weaknesses.
Defining a date range by specifying years allows for more thorough and systematic backtesting, helping traders develop more reliable and effective trading systems.
Alan's Date Range Calculator: A TradingView Pine Script Indicator
Purpose
This Pine Script indicator calculates and displays a date range for backtesting trading strategies. It allows users to specify the number of years to analyze and an end date, then calculates the corresponding start date. Most importantly, users can copy the inputs and function into their own strategies to quickly add a time span feature for backtesting.
Key Features
User-defined input for the number of years to analyze
Customizable end date with a calendar input
Automatic calculation of the start date
Visual display of both start and end dates on the chart
How It Works
User Inputs
Years of Data to Process: An integer input allowing users to specify the number of years for analysis (default: 20, range: 1-100)
End Date: A calendar input for selecting the end date of the analysis period (default: December 31, 2024)
Date Calculation
The script uses a custom function calcStartDate() to determine the start date. It subtracts the specified number of years from the end date's year and sets the start date to January 1st of that year.
Visual Output
The indicator displays two labels on the chart:
Start Date Label: Shows the calculated start date
End Date Label: Displays the user-specified end date
Both labels are positioned horizontally at the bottom of the chart, with the end date label to the right of the start date label.
Applications
This indicator is particularly useful for traders who want to:
Define specific date ranges for backtesting strategies
Quickly visualize the time span of their analysis
Ensure consistent testing periods across different strategies or assets
Customization
Users can easily adjust the analysis period by changing the number of years or selecting a different end date. This flexibility allows for testing strategies across various market conditions and time frames.
XAUUSD 10-Minute StrategyThis XAUUSD 10-Minute Strategy is designed for trading Gold vs. USD on a 10-minute timeframe. By combining multiple technical indicators (MACD, RSI, Bollinger Bands, and ATR), the strategy effectively captures both trend-following and reversal opportunities, with adaptive risk management for varying market volatility. This approach balances high-probability entries with robust volatility management, making it suitable for traders seeking to optimise entries during significant price movements and reversals.
Key Components and Logic:
MACD (12, 26, 9):
Generates buy signals on MACD Line crossovers above the Signal Line and sell signals on crossovers below the Signal Line, helping to capture momentum shifts.
RSI (14):
Utilizes oversold (below 35) and overbought (above 65) levels as a secondary filter to validate entries and avoid overextended price zones.
Bollinger Bands (20, 2):
Uses upper and lower Bollinger Bands to identify potential overbought and oversold conditions, aiming to enter long trades near the lower band and short trades near the upper band.
ATR-Based Stop Loss and Take Profit:
Stop Loss and Take Profit levels are dynamically set as multiples of ATR (3x for stop loss, 5x for take profit), ensuring flexibility with market volatility to optimise exit points.
Entry & Exit Conditions:
Buy Entry: T riggered when any of the following conditions are met:
MACD Line crosses above the Signal Line
RSI is oversold
Price drops below the lower Bollinger Band
Sell Entry: Triggered when any of the following conditions are met:
MACD Line crosses below the Signal Line
RSI is overbought
Price moves above the upper Bollinger Band
Exit Strategy: Trades are closed based on opposing entry signals, with adaptive spread adjustments for realistic exit points.
Backtesting Configuration & Results:
Backtesting Period: July 21, 2024, to October 30, 2024
Symbol Info: XAUUSD, 10-minute timeframe, OANDA data source
Backtesting Capital: Initial capital of $700, with each trade set to 10 contracts (equivalent to approximately 0.1 lots based on the broker’s contract size for gold).
Users should confirm their broker's contract size for gold, as this may differ. This script uses 10 contracts for backtesting purposes, aligned with 0.1 lots on brokers offering a 100-contract specification.
Key Backtesting Performance Metrics:
Net Profit: $4,733.90 USD (676.27% increase)
Total Closed Trades: 526
Win Rate: 53.99%
Profit Factor: 1.44 (1.96 for Long trades, 1.14 for Short trades)
Max Drawdown: $819.75 USD (56.33% of equity)
Sharpe Ratio: 1.726
Average Trade: $9.00 USD (0.04% of equity per trade)
This backtest reflects realistic conditions, with a spread adjustment of 38 points and no slippage or commission applied. The settings aim to simulate typical retail trading conditions. However, please adjust the initial capital, contract size, and other settings based on your account specifics for best results.
Usage:
This strategy is tuned specifically for XAUUSD on a 10-minute timeframe, ideal for both trend-following and reversal trades. The ATR-based stop loss and take profit levels adapt dynamically to market volatility, optimising entries and exits in varied conditions. To backtest this script accurately, ensure your broker’s contract specifications for gold align with the parameters used in this strategy.
All Divergences with trend / SL - Uncle SamThanks to the main inspiration behind this strategy and the hard work of:
"Divergence for many indicators v4 by LonesomeTheBlue"
The "All Divergence" strategy is a versatile approach for identifying and acting upon various divergences in the market. Divergences occur when price and an indicator move in opposite directions, often signaling potential reversals. This strategy incorporates both regular and hidden divergences across multiple indicators (MACD, Stochastics, CCI, etc.) for a comprehensive analysis.
Key Features:
Comprehensive Divergence Analysis: The strategy scans for regular and hidden divergences across a variety of indicators, increasing the probability of identifying potential trade setups.
Trend Filter: To enhance accuracy, a moving average (MA) trend filter is integrated. This ensures trades align with the overall market trend, reducing the risk of false signals.
Customizable Risk Management: Users can adjust parameters for long/short stop-loss and take-profit levels to match their individual risk tolerance.
Additional Risk Management (Optional): An experimental MA-based risk management feature can be enabled to close positions if the market shows consecutive closes against the trend.
Clear Visuals: The script plots pivot points, divergence lines, and stop-loss levels on the chart for easy reference.
Strategy Settings (Defaults):
Enable Long/Short Strategy: True
Long/Short Stop Loss %: 2%
Long/Short Take Profit %: 5%
Enable MA Trend: True
MA Type: HMA (Hull Moving Average)
MA Length: 500
Use MA Risk Management: False (Experimental)
MA Risk Exit Candles: 2 (If enabled)
Pivot Period: 9
Source for Pivot Points: Close
Backtest Details (Example):
The strategy has been backtested on XAUUSD 1H (Goold/USD 1 hour timeframe) with a starting capital of $1,000. The backtest period covers around 2 years. A commission of 0.02% per trade and a 0.1% slippage per trade were factored in to simulate real-world trading costs.
Disclaimer:
This strategy is for educational and informational purposes only. Backtested results are not indicative of future performance. Use this strategy at your own risk. Always conduct your own analysis and consider consulting a financial professional before making any trading decisions.
Important Notes:
The default settings are a good starting point, but feel free to experiment to find optimal parameters for your specific trading style and market.
The MA-based risk management is an experimental feature. Use it with caution and thoroughly test it before deploying in live trading.
Backtest results can vary depending on the market, timeframe, and specific settings used. Always consider slippage and commission fees when evaluating a strategy's potential profitability.
ADX + CCI + MA - Uncle SamStrategy Name: ADX + CCI + MA - Uncle Sam
Overview
This strategy aims to capitalize on trending markets by combining the Average Directional Index (ADX), Commodity Channel Index (CCI), and a customizable Moving Average (MA). It's designed for traders seeking a balanced approach to both long (buy) and short (sell) opportunities. Special thanks to the creators of the ADX and CCI indicators for their invaluable contributions to technical analysis.
Strategy Concept
The core idea is to identify strong trends with the ADX, confirm potential entry points with the CCI, and use the MA to filter trades in the direction of the broader trend. This approach seeks to avoid entering positions during periods of consolidation or when the trend is weak.
Indicator Logic
ADX (Average Directional Index): The ADX measures the strength of a trend, regardless of its direction. A value above the customizable adx_threshold (default 20) signals a strong trend, making it a prime environment for this strategy.
CCI (Commodity Channel Index): The CCI is a momentum oscillator that helps identify overbought (above 100) and oversold (below -100) conditions. We use CCI crossovers to time entries in the direction of the prevailing trend.
MA (Moving Average): The MA acts as a trend filter, ensuring we only enter trades aligned with the overall market direction. You have flexibility in choosing the MA type (SMA, EMA, etc.) and its length to suit your trading style and timeframe.
Entry Conditions
Long (Buy):
ADX is above the adx_threshold.
CCI crosses above 100.
Price is above the chosen Moving Average (if MA trend filtering is enabled).
Short (Sell):
ADX is above the adx_threshold.
CCI crosses below -100.
Price is below the chosen Moving Average (if MA trend filtering is enabled).
Exit Conditions
Stop Loss (SL): Each position has a customizable stop-loss percentage to manage risk. The default setting is 1%.
Take Profit (TP): Each position has a customizable take-profit percentage to secure gains. The default setting is 5%.
MA-Based Risk Management (Optional): This feature allows for early exits if the price closes against the MA trend for a specified number of candles. The default setting is 2 candles.
Default Settings
CCI Period: 15
ADX Length: 10
ADX Threshold: 20
MA Type: HMA
MA Length: 200
MA Source: Close
Commission Fee: $0.0
A commission fee is not added, add your trading/platform commission for realistic trading costs.
Backtest Results
The strategy has been backtested on with the default settings and a starting capital of $1000, with 0.0% commission fee. It shows promising results.
Disclaimer: Backtesting is hypothetical and does not guarantee future performance.
Important Considerations:
Customization: The strategy offers extensive customization to tailor it to your preferences. Experiment with different parameters and settings to find what works best for your trading style.
Risk Management: Always use proper risk management techniques, including position sizing and stop losses, to protect your capital.
Candles HTF on Heikin Ashi ChartThis script enables calling and/or plotting of traditional Candles sources while loaded on Heikin Ashi charts.
Thanks to @PineCoders for rounding method: www.pinecoders.com
Thanks to @BeeHolder for method to regex normalize syminfo.tickerid.
NOTICE: While this script is meant to be utilized on Heikin Ashi charts it does NOT enable ability to backtest!
NOTICE: For more info on why non standard charts cannot be reliably backtested please see:
NOTICE: This is an example script and not meant to be used as an actual strategy. By using this script or any portion thereof, you acknowledge that you have read and understood that this is for research purposes only and I am not responsible for any financial losses you may incur by using this script!
Super Trend Daily 2.0 Alerts BFThis is an alerts script for my Super Trend 2.0 indicator . It is intended as a companion script so you can backtest using the Strategy script and generate alerts using this Study script.
This Study script has the same default settings as the Strategy script and its only purpose is to provide alerts for the long and short signals the Strategy generates. Obviously, if you want to generate alerts based on a Strategy backtest, please ensure the settings are the same in the Study as in the Strategy.
For illustration, I have plotted arrows on the chart for long and short signals, and also colored the background to show when the rate of change function determines a choppy/sideways market.
ALERTS
There are 2 alerts set up:
Long Entry
Short Entry
ILLUSTRATION
Green arrow = Long Entry
Red arrow = Short Entry
White background = No short trades
Aqua background = No long trades
EXAMPLE USE CASE
1. Open a Bitcoin/USD chart on 1D timeframe.
2. Open this script and the Super Trend 2.0 indicator script.
3. Backtest with the Strategy Backtester and change the settings if you like until you get a desirable outcome for your own purposes.
4. Once you are happy with the backtest, change the settings in the Alerts script (this one) so they match the Strategy settings.
5. Set up the alerts according to your preferences.
Grand Master's Candlestick Dominance (ATR Enhanced)### Grand Master's Candlestick Dominance (ATR Enhanced)
**Overview**
Unleash the ancient wisdom of Japanese candlestick charting with a modern twist! This comprehensive Pine Script v5 strategy and indicator scans for over 75 classic and advanced candlestick patterns (bullish, bearish, and neutral), assigning dynamic strength scores (1-10) to each for precise signal filtering. Enhanced with Average True Range (ATR) for volatility-aware body size validation, it dominates the markets by combining timeless pattern recognition with robust confirmation layers. Whether used as a backtestable strategy or visual indicator, it empowers traders to spot high-probability reversals, continuations, and indecision setups with surgical accuracy.
Inspired by Steve Nison's *Japanese Candlestick Charting Techniques*, this tool elevates pattern analysis beyond basics—think Hammers, Engulfing patterns, Morning Stars, and rare gems like Abandoned Baby or Concealing Baby Swallow—all consolidated into intelligent arrays for real-time averaging and prioritization.
**Key Features**
- **Extensive Pattern Library**:
- **Bullish (25+ patterns)**: Hammer (8.0), Bullish Engulfing (10.0), Morning Star (7.0), Three White Soldiers (9.0), Dragonfly Doji (8.0), and more (e.g., Rising Three, Unique Three River Bottom).
- **Bearish (25+ patterns)**: Hanging Man (8.0), Bearish Engulfing (10.0), Evening Star (7.0), Three Black Crows (9.0), Gravestone Doji (8.0), and exotics like Upside Gap Two Crows or Stalled Pattern.
- **Neutral/Indecision (34+ patterns)**: Doji variants (Long-Legged, Four Price), Spinning Tops, Harami Crosses, and multi-bar setups like Upside Tasuki Gap or Advancing Block.
Each pattern includes duration tracking (1-5 bars) and ATR-adjusted body/shadow criteria for relevance in volatile conditions.
- **Smart Confirmation Filters** (All Toggleable):
- **Trend Alignment**: 20-period SMA (customizable) ensures entries align with the prevailing trend; optional higher timeframe (e.g., Daily) MA crossover for multi-timeframe confluence.
- **Support/Resistance (S/R)**: Pivot-based levels with 0.01% tolerance to confirm bounces or breaks.
- **Volume Surge**: 20-period volume MA with 1.5x spike multiplier to validate momentum.
- **ATR Body Sizing**: Filters small bodies (<0.3x ATR) and long bodies (>0.8x ATR) for context-aware pattern reliability.
- **Follow-Through**: Ensures post-pattern confirmation via bullish/bearish closes or closes beyond prior bars.
Minimum average strength (default 7.0) and individual pattern thresholds (5.0) prevent weak signals.
- **Entry & Exit Logic**:
- **Long Entry**: Bullish average strength ≥7.0 (outweighing bearish), uptrend, volume spike, near support, follow-through, and HTF alignment.
- **Short Entry**: Mirror for bearish dominance in downtrends near resistance.
- **Exits**: Bearish/neutral shift, or fixed TP (5%) / SL (2%)—pyramiding disabled, 10% equity sizing.
- Backtest range: Jan 1, 2020 – Dec 31, 2025 (editable). Initial capital: $10,000.
- **Interactive Dashboard** (Top-Right Panel):
Real-time insights including:
- Market phase (e.g., "Bullish Phase (Avg Str: 8.2)"), active pattern (e.g., "BULLISH: Bullish Engulfing (Str: 10.0, Bars: 2)"), and trend status.
- Strength breakdowns (Bull/Bear/Neutral counts & averages).
- Filter status (e.g., "Volume: ✔ Spike", "ATR: Enabled (L:0.8, S:0.3)").
- Backtest stats: Total trades, win rate, streak, and last entry/exit details (price & timestamp).
Toggle mode: Strategy (live trades) or Indicator (signals only).
- **Advanced Alerts** (15+ Toggleable Types):
Set up via TradingView's "Any alert() function call" for bar-close triggers:
- Entry/Exit signals with strength & pattern details.
- Strong patterns (≥2 bullish/bearish), neutral indecision, volume spikes.
- S/R breakouts, HTF reversals, high-confidence singles (≥8.0 strength).
- Conflicting signals, MA crossovers, ATR volatility bursts, multi-bar completions.
Example: "STRONG BULLISH PATTERN detected! Strength: 9.5 | Top Pattern: Three White Soldiers | Trend: Up".
**Customization & Usage Tips**
- **Inputs Groups**: Strategy toggles, confirmations, exits, backtest dates, and 15+ alert switches—all intuitively grouped.
- **Optimization**: Tune min strengths for aggressive (lower) or conservative (higher) trading; enable/disable filters to suit your style (e.g., disable S/R for scalping).
- **Best For**: Forex, stocks, crypto on 1H–Daily charts. Test on historical data to refine TP/SL.
- **Limitations**: No external data installs; relies on built-in TA functions. Patterns are probabilistic—combine with your risk management.
Master the candles like a grandmaster. Deploy on TradingView, backtest relentlessly, and let dominance begin! Questions? Drop a comment.
*Version: 1.0 | Updated: September 2025 | Credits: Built on Pine Script v5 with nods to Nison's timeless techniques.*
Advanced Multi-Timeframe Trading System (Risk Managed)Description:
This strategy is an original approach that combines two main analytical components to identify potential trade opportunities while simulating realistic trading conditions:
1. Market Trend Analysis via an Approximate Hurst Exponent
• What It Does:
The strategy computes a rough measure of market trending using an approximate Hurst exponent. A value above 0.5 suggests persistent, trending behavior, while a value below 0.5 indicates a tendency toward mean-reversion.
• How It’s Used:
The Hurst exponent is calculated on both the chart’s current timeframe and a higher timeframe (default: Daily) to capture both local and broader market dynamics.
2. Fibonacci Retracement Levels
• What It Does:
Using daily high and low data from a selected timeframe (default: Daily), the script computes key Fibonacci retracement levels.
• How It’s Used:
• The 61.8% level (Golden Ratio) serves as a key threshold:
• A long entry is signaled when the price crosses above this level if the daily Hurst exponent confirms a trending market.
• The 38.2% level is used to identify short-entry opportunities when the price crosses below it and the daily Hurst indicates non-trending conditions.
Signal Logic:
• Long Entry:
When the price crosses above the 61.8% Fibonacci level (Golden Ratio) and the daily Hurst exponent is greater than 0.5, suggesting a trending market.
• Short Entry:
When the price crosses below the 38.2% Fibonacci level and the daily Hurst exponent is less than 0.5, indicating a less trending or potentially reversing market.
Risk Management & Trade Execution:
• Stop-Loss:
Each trade is risk-managed with a stop-loss set at 2% below (for longs) or above (for shorts) the entry price. This ensures that no single trade risks more than a small, sustainable portion of the account.
• Take Profit:
A take profit order targets a risk-reward ratio of 1:2 (i.e., the target profit is twice the amount risked).
• Position Sizing:
Trades are executed with a fixed position size equal to 10% of account equity.
• Trade Frequency Limits:
• Daily Limit: A maximum of 5 trades per day
• Overall Limit: No more than 510 trades during the backtesting period (e.g., since 2019)
These limits are imposed to simulate realistic trading frequency and to avoid overtrading in backtest results.
Backtesting Parameters:
• Initial Capital: $10,000
• Commission: 0.1% per trade
• Slippage: 1 tick per bar
These settings aim to reflect the conditions faced by the average trader and help ensure that the backtesting results are realistic and not misleading.
Chart Overlays & Visual Aids:
• Fibonacci Levels:
The key Fibonacci retracement levels are plotted on the chart, and the zone between the 61.8% and 38.2% levels is highlighted to show a key retracement area.
• Market Trend Background:
The chart background is tinted green when the daily Hurst exponent indicates a trending market (value > 0.5) and red otherwise.
• Information Table:
An on-chart table displays key parameters such as the current Hurst exponent, daily Hurst value, the number of trades executed today, and the global trade count.
Disclaimer:
Past performance is not indicative of future results. This strategy is experimental and provided solely for educational purposes. It is essential that you backtest and paper trade using your own settings before considering any live deployment. The Hurst exponent calculation is an approximation and should be interpreted as a rough gauge of market behavior. Adjust the parameters and risk management settings according to your personal risk tolerance and market conditions.
Additional Notes:
• Originality & Usefulness:
This script is an original mashup that combines trend analysis with Fibonacci retracement methods. The description above explains how these components work together to provide trading signals.
• Realistic Results:
The strategy uses realistic account sizes, commission rates, slippage, and risk management rules to generate backtesting results that are representative of real-world trading.
• Educational Purpose:
This script is intended to support the TradingView community by offering insights into combining multiple analysis techniques in one strategy. It is not a “get-rich-quick” system but rather an educational tool to help traders understand risk management and trade signal logic.
By using this script, you acknowledge that trading involves risk and that you are responsible for testing and adjusting the strategy to fit your own trading environment. This publication is fully open source, and any modifications should include proper attribution if significant portions of the code are reused.
DNSE VN301!, SMA & EMA Cross StrategyDiscover the tailored Pinescript to trade VN30F1M Future Contracts intraday, the strategy focuses on SMA & EMA crosses to identify potential entry/exit points. The script closes all positions by 14:25 to avoid holding any contracts overnight.
HNX:VN301!
www.tradingview.com
Setting & Backtest result:
1-minute chart, initial capital of VND 100 million, entering 4 contracts per time, backtest result from Jan-2024 to Nov-2024 yielded a return over 40%, executed over 1,000 trades (average of 4 trades/day), winning trades rate ~ 30% with a profit factor of 1.10.
The default setting of the script:
A decent optimization is reached when SMA and EMA periods are set to 60 and 15 respectively while the Long/Short stop-loss level is set to 20 ticks (2 points) from the entry price.
Entry & Exit conditions:
Long signals are generated when ema(15) crosses over sma(60) while Short signals happen when ema(15) crosses under sma(60). Long orders are closed when ema(15) crosses under sma(60) while Short orders are closed when ema(15) crosses over sma(60).
Exit conditions happen when (whichever came first):
Another Long/Short signal is generated
The Stop-loss level is reached
The Cut-off time is reached (14:25 every day)
*Disclaimers:
Futures Contracts Trading are subjected to a high degree of risk and price movements can fluctuate significantly. This script functions as a reference source and should be used after users have clearly understood how futures trading works, accessed their risk tolerance level, and are knowledgeable of the functioning logic behind the script.
Users are solely responsible for their investment decisions, and DNSE is not responsible for any potential losses from applying such a strategy to real-life trading activities. Past performance is not indicative/guarantee of future results, kindly reach out to us should you have specific questions about this script.
---------------------------------------------------------------------------------------
Khám phá Pinescript được thiết kế riêng để giao dịch Hợp đồng tương lai VN30F1M trong ngày, chiến lược tập trung vào các đường SMA & EMA cắt nhau để xác định các điểm vào/ra tiềm năng. Chiến lược sẽ đóng tất cả các vị thế trước 14:25 để tránh giữ bất kỳ hợp đồng nào qua đêm.
Thiết lập & Kết quả backtest:
Chart 1 phút, vốn ban đầu là 100 triệu đồng, vào 4 hợp đồng mỗi lần, kết quả backtest từ tháng 1/2024 tới tháng 11/2024 mang lại lợi nhuận trên 40%, thực hiện hơn 1.000 giao dịch (trung bình 4 giao dịch/ngày), tỷ lệ giao dịch thắng ~ 30% với hệ số lợi nhuận là 1,10.
Thiết lập mặc định của chiến lược:
Đạt được một mức tối ưu ổn khi SMA và EMA periods được đặt lần lượt là 60 và 15 trong khi mức cắt lỗ được đặt thành 20 tick (2 điểm) từ giá vào.
Điều kiện Mở và Đóng vị thế:
Tín hiệu Long được tạo ra khi ema(15) cắt trên sma(60) trong khi tín hiệu Short xảy ra khi ema(15) cắt dưới sma(60). Lệnh Long được đóng khi ema(15) cắt dưới sma(60) trong khi lệnh Short được đóng khi ema(15) cắt lên sma(60).
Điều kiện đóng vị thể xảy ra khi (tùy điều kiện nào đến trước):
Một tín hiệu Long/Short khác được tạo ra
Giá chạm mức cắt lỗ
Lệnh chưa đóng nhưng tới giờ cut-off (14:25 hàng ngày)
*Tuyên bố miễn trừ trách nhiệm:
Giao dịch hợp đồng tương lai có mức rủi ro cao và giá có thể dao động đáng kể. Chiến lược này hoạt động như một nguồn tham khảo và nên được sử dụng sau khi người dùng đã hiểu rõ cách thức giao dịch hợp đồng tương lai, đã đánh giá mức độ chấp nhận rủi ro của bản thân và hiểu rõ về logic vận hành của chiến lược này.
Người dùng hoàn toàn chịu trách nhiệm về các quyết định đầu tư của mình và DNSE không chịu trách nhiệm về bất kỳ khoản lỗ tiềm ẩn nào khi áp dụng chiến lược này vào các hoạt động giao dịch thực tế. Hiệu suất trong quá khứ không chỉ ra/cam kết kết quả trong tương lai, vui lòng liên hệ với chúng tôi nếu bạn có thắc mắc cụ thể về chiến lược giao dịch này.
Hyperbolic Tangent Volatility Stop [InvestorUnknown]The Hyperbolic Tangent Volatility Stop (HTVS) is an advanced technical analysis tool that combines the smoothing capabilities of the Hyperbolic Tangent Moving Average (HTMA) with a volatility-based stop mechanism. This indicator is designed to identify trends and reversals while accounting for market volatility.
Hyperbolic Tangent Moving Average (HTMA):
The HTMA is at the heart of the HTVS. This custom moving average uses a hyperbolic tangent transformation to smooth out price fluctuations, focusing on significant trends while ignoring minor noise. The transformation reduces the sensitivity to sharp price movements, providing a clearer view of the underlying market direction.
The hyperbolic tangent function (tanh) is commonly used in mathematical fields like calculus, machine learning and signal processing due to its properties of “squashing” inputs into a range between -1 and 1. The function provides a non-linear transformation that can reduce the impact of extreme values while retaining a certain level of smoothness.
tanh(x) =>
e_x = math.exp(x)
e_neg_x = math.exp(-x)
(e_x - e_neg_x) / (e_x + e_neg_x)
The HTMA is calculated by applying a non-linear transformation to the difference between the source price and its simple moving average, then adjusting it using the standard deviation of the price data. The result is a moving average that better tracks the real market direction.
htma(src, len, mul) =>
tanh_src = tanh((src - ta.sma(src, len)) * mul) * ta.stdev(src, len) + ta.sma(src, len)
htma = ta.sma(tanh_src, len)
Important Note: The Hyperbolic Tangent function becomes less accurate with very high prices. For assets priced above 100,000, the results may deteriorate, and for prices exceeding 1 million, the function may stop functioning properly. Therefore, this indicator is better suited for assets with lower prices or lower price ratios.
Volatility Stop (VolStop):
HTVS employs a Volatility Stop mechanism based on the Average True Range (ATR). This stop dynamically adjusts based on market volatility, ensuring that the indicator adapts to changing conditions and avoids false signals in choppy markets.
The VolStop follows the price, with a higher ATR pushing the stop farther away to avoid premature exits during volatile periods. Conversely, when volatility is low, the stop tightens to lock in profits as the trend progresses.
The ATR Length and ATR Multiplier are customizable, allowing traders to control how tightly or loosely the stop follows the price.
pine_volStop(src, atrlen, atrfactor) =>
if not na(src)
var max = src
var min = src
var uptrend = true
var float stop = na
atrM = nz(ta.atr(atrlen) * atrfactor, ta.tr)
max := math.max(max, src)
min := math.min(min, src)
stop := nz(uptrend ? math.max(stop, max - atrM) : math.min(stop, min + atrM), src)
uptrend := src - stop >= 0.0
if uptrend != nz(uptrend , true)
max := src
min := src
stop := uptrend ? max - atrM : min + atrM
Backtest Mode:
HTVS includes a built-in backtest mode, allowing traders to evaluate the indicator's performance on historical data. In backtest mode, it calculates the cumulative equity curve and compares it to a simple buy and hold strategy.
Backtesting features can be adjusted to focus on specific signal types, such as Long Only, Short Only, or Long & Short.
An optional Buy and Hold Equity plot provides insight into how the indicator performs relative to simply holding the asset over time.
The indicator includes a Hints Table, which provides useful recommendations on how to best display the indicator for different use cases. For example, when using the overlay mode, it suggests displaying the indicator in the same pane as price action, while backtest mode is recommended to be used in a separate pane for better clarity.
The Hyperbolic Tangent Volatility Stop offers traders a balanced approach to trend-following, using the robustness of the HTMA for smoothing and the adaptability of the Volatility Stop to avoid whipsaw trades during volatile periods. With its backtesting features and alert system, this indicator provides a comprehensive toolkit for active traders.
RSI SMA Crossover StrategyOverview
RSI SMA Crossover Strategy works the same way as traditional MA crossover strategies, but using RSI instead of price. When RSI crosses over the SMA, a long position is opened (buy). When RSI crosses under the SMA, the long position is closed (sell).
This strategy can be very effective when the right inputs are used (see below). Be sure to use the backtesting tool to determine the optimal parameters for a given asset/timeframe.
Inputs/Parameters
RSI Length: length for RSI calculation (default = 50)
SMA Length: length for SMA calculation (default = 25)
Strategy Properties
Initial Capital = $1000
No default properties are defined for Slippage, Commission, etc, so be sure to set these values to get accurate backtesting results. This script is being published open-source for a reason - save yourself a copy and adjust the settings as you like!
Backtesting Results
Testing on Bitcoin (all time index) 1D chart, with all default parameters.
$1,000 initial investment on 10/07/2010 turns into almost $2.5 billion as of 08/30/2022 (compared to $334 million if the initial investment was held over the same period)
Remember, results can vary greatly based on the variables mentioned above, so always be sure to backtest.
Stochastic Pop and Drop by Jake Bernstein v1 [Bitduke]I found a simple strategy by Jake Bernstein, modified it a little and created a strategy with Risk Management System (SL+TP); After that I test it on the different cryptocurrency pairs.
About the Indicator
Basically it's the strategy of 2 indicators: Stochastic Oscillator to define the bias and Average Directional Index to confirm it.
One again, It uses Stochastic Oscillator to define the trading bias. In particular, the trading bias was deemed bullish when the weekly 14-period Stochastic Oscillator was above some default value (in him paper - 50) and rising and vice versa.
Once the trading bias is established, Steckler used the Average Directional Index (ADX) to define a slowdown in the trend. ADX measures the strength of the trend and a move below 20 signals a weak trend.
Modifications
I didn't implement Average Directional Index (ADX) and test just different sources for data, oscillator periods and different levels in relation to the crypto market.
So, it shows good results with two tight thresholds at 55 and 45 level.
The bar chart below the defining the bullish and bearish periods (green and red) and gives a signal to enter the trade (purple bars).
Backtesting
Backtested on XBTUSD , BTCPERP (FTX) pairs. You may notice it shows good results on 3h timeframe.
Relatively low drawdown
~ 10% (from 2019 to date) FTX
~ 22% (4 years from 2016) Bitmex
I backtested on the different altcoin pairs as well, but the results were just not good.
Relatively good results were shown by some index pairs from the FTX exchange ( FTX:SHITPERP ), but I think there is a few data for backtesting to be asure in them.
Bitmex 3h (2017 - 2020) :
i.imgur.com
FTX 3h (2019 - 2020):
i.imgur.com
Possible Improvements
- Regarding trading algorithm it would be good to check with strategy with ADX somehow. Maybe for the better entries
- As for Risk Management system, it can be improved by adding trailing stop to the strategy.
Link: school.stockcharts.com
RSI ADX Bollinger Analysis High-level purpose and design philosophy
This indicator — RSI-ADX-Bollinger Analysis — is a compact, educational market-analysis toolkit that blends momentum (RSI), trend strength (ADX), volatility structure (Bollinger Bands) and simple volumetrics to provide traders a snapshot of market condition and trade idea quality. The design philosophy is explicit and layered: use each component to answer a different question about price action (momentum, conviction, volatility, participation), then combine answers to form a more robust, explainable signal. The mashup is intended for analysis and learning, not automatic execution: it surfaces the why behind signals so traders can test, learn and apply rules with risk management.
________________________________________
What each indicator contributes (component-by-component)
RSI (Relative Strength Index) — role and behavior: RSI measures short-term momentum by comparing recent gains to recent losses. A high RSI (near or above the overbought threshold) indicates strong recent buying pressure and potential exhaustion if price is extended. A low RSI (near or below the oversold threshold) indicates strong recent selling pressure and potential exhaustion or a value area for mean-reversion. In this dashboard RSI is used as the primary momentum trigger: it helps identify whether price is locally over-extended on the buy or sell side.
ADX (Average Directional Index) — role and behavior: ADX measures trend strength independently of direction. When ADX rises above a chosen threshold (e.g., 25), it signals that the market is trending with conviction; ADX below the threshold suggests range or weak trend. Because patterns and momentum signals perform differently in trending vs. ranging markets, ADX is used here as a filter: only when ADX indicates sufficient directional strength does the system treat RSI+BB breakouts as meaningful trade candidates.
Bollinger Bands — role and behavior: Bollinger Bands (20-period basis ± N standard deviations) show volatility envelope and relative price position vs. a volatility-adjusted mean. Price outside the upper band suggests pronounced extension relative to recent volatility; price outside the lower band suggests extended weakness. A band expansion (increasing width) signals volatility breakout potential; contraction signals range-bound conditions and potential squeeze. In this dashboard, Bollinger Bands provide the volatility/structural context: RSI extremes plus price beyond the band imply a stronger, volatility-backed move.
Volume split & basic MA trend — role and behavior: Buy-like and sell-like volume (simple heuristic using close>open or closeopen) or sell-like (close1.2 for validation and compare win rate and expectancy.
4. TF alignment: Accept signals only when higher timeframe (e.g., 4h) trend agrees — compare results.
5. Parameter sensitivity: Vary RSI threshold (70/30 vs 80/20), Bollinger stddev (2 vs 2.5), and ADX threshold (25 vs 30) and measure stability of results.
These exercises teach both statistical thinking and the specific failure modes of the mashup.
________________________________________
Limitations, failure modes and caveats (explicit & teachable)
• ADX and Bollinger measures lag during fast-moving news events — signals can be late or wrong during earnings, macro shocks, or illiquid sessions.
• Volume classification by open/close is a heuristic; it does not equal TAPEDATA, footprint or signed volume. Use it as supportive evidence, not definitive proof.
• RSI can remain overbought or oversold for extended stretches in persistent trends — relying solely on RSI extremes without ADX or BB context invites large drawdowns.
• Small-cap or low-liquidity instruments yield noisy band behavior and unreliable volume ratios.
Being explicit about these limitations is a strong point in a TradingView description — it demonstrates transparency and educational intent.
________________________________________
Originality & mashup justification (text you can paste)
This script intentionally combines classical momentum (RSI), volatility envelope (Bollinger Bands) and trend-strength (ADX) because each indicator answers a different and complementary question: RSI answers is price locally extreme?, Bollinger answers is price outside normal volatility?, and ADX answers is the market moving with conviction?. Volume participation then acts as a practical check for real market involvement. This combination is not a simple “indicator mashup”; it is a designed ensemble where each element reduces the others’ failure modes and together produce a teachable, testable signal framework. The script’s purpose is educational and analytical — to show traders how to interpret the interplay of momentum, volatility, and trend strength.
________________________________________
TradingView publication guidance & compliance checklist
To satisfy TradingView rules about mashups and descriptions, include the following items in your script description (without exposing source code):
1. Purpose statement: One or two lines describing the script’s objective (educational multi-indicator market overview and idea filter).
2. Component list: Name the major modules (RSI, Bollinger Bands, ADX, volume heuristic, SMA trend checks, signal tracking) and one-sentence reason for each.
3. How they interact: A succinct non-code explanation: “RSI finds momentum extremes; Bollinger confirms volatility expansion; ADX confirms trend strength; all three must align for a BUY/SELL.”
4. Inputs: List adjustable inputs (RSI length and thresholds, BB length & stddev, ADX threshold & smoothing, volume MA, table position/size).
5. Usage instructions: Short workflow (check TF alignment → confirm participation → define stop & R:R → backtest).
6. Limitations & assumptions: Explicitly state volume is approximated, ADX has lag, and avoid promising guaranteed profits.
7. Non-promotional language: No external contact info, ads, claims of exclusivity or guaranteed outcomes.
8. Trademark clause: If you used trademark symbols, remove or provide registration proof.
9. Risk disclaimer: Add the copy-ready disclaimer below.
This matches TradingView’s request for meaningful descriptions that explain originality and inter-component reasoning.
________________________________________
Copy-ready short publication description (paste into TradingView)
Advanced RSI-ADX-Bollinger Market Overview — educational multi-indicator dashboard. This script combines RSI (momentum extremes), Bollinger Bands (volatility envelope and band expansion), ADX (trend strength), simple SMA trend bias and a basic buy/sell volume heuristic to surface high-quality idea candidates. Signals require alignment of momentum, volatility expansion and rising ADX; volume participation is displayed to support signal confidence. Inputs are configurable (RSI length/levels, BB length/stddev, ADX length/threshold, volume MA, display options). This tool is intended for analysis and learning — not for automated execution. Users should back test and apply robust risk management. Limitations: volume classification here is a heuristic (close>open), ADX and BB measures lag in fast news events, and results vary by instrument liquidity.
________________________________________
Copy-ready risk & misuse disclaimer (paste into description or help file)
This script is provided for educational and analytical purposes only and does not constitute financial or investment advice. It does not guarantee profits. Indicators are heuristics and may give false or late signals; always back test and paper-trade before using real capital. The author is not responsible for trading losses resulting from the use or misuse of this indicator. Use proper position sizing and risk controls.
________________________________________
Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
AVWAP+RSI Confluence — 1R TesterRSI + 1R ATR - Monthly P\&L (v4)
WHAT THIS STRATEGY DOES (OVERVIEW)
* Pine strategy (v4) that combines a simple momentum trigger with a symmetric 1R ATR risk model and an on-chart Monthly/Yearly P\&L table.
* Momentum filter: trades only when RSI crosses its own SMA in the direction of the trend (price vs Trend EMA).
* Risk engine: exits use fixed 1R ATR brackets captured at entry (no drifting targets/stops).
* Accounting: the table aggregates percentage returns by month and year using strategy equity.
ENTRY LOGIC (LONGS & OPTIONAL SHORTS)
Indicators used:
* RSI(rsiLen) and its SMA: SMA(RSI, rsiMaLen)
* Trend filter: EMA(emaTrendLen) on price
Longs:
1. RSI crosses above its RSI SMA
2. RSI > rsiBuyThr (filters weak momentum)
3. Close > EMA(emaTrendLen)
Shorts (optional via enableShort):
1. RSI crosses below its RSI SMA
2. RSI < rsiSellThr
3. Close < EMA(emaTrendLen)
EXIT LOGIC AND RISK MODEL (1R ATR)
* On entry, snapshot ATR(atrLen) into atrAtEntry and the average fill price into entryPx.
* Longs: stop = entryPx - ATR \* atrMult; target = entryPx + ATR \* atrMult
* Shorts: mirrored.
* Stops and targets are posted immediately and remain fixed for the life of the trade.
POSITION SIZING AND COSTS
* Default position size: 25% of equity per trade (adjustable in Properties/inputs).
* Commission percent and a small slippage are set in strategy() so backtests include friction by default.
MONTHLY / YEARLY P\&L TABLE (HOW IT WORKS)
* Uses strategy equity to compute bar returns: equity / equity\ - 1.
* Compounds bar returns into current month and current year; commits each finished period at month/year change (or last bar).
* Renders rows as years; columns Jan..Dec plus a Year total column.
* Cells colored by sign; precision and maximum rows are controlled by inputs.
* Values represent percentage returns, not currency P\&L.
VISUAL AIDS
* Two pivot trails (pivot high/low) are plotted for context only; they do not affect entries or exits.
CUSTOMIZATION TIPS
* Raise rsiBuyThr (long) or lower rsiSellThr (short) to filter weak momentum.
* Increase emaTrendLen to tighten trend alignment.
* Adjust atrLen and atrMult to fit your timeframe/instrument volatility.
* Leave enableShort = false if you prefer long-only behavior or shorting is constrained.
NON-REPAINTING AND BACKTEST NOTES
* Signals use bar-close crosses of built-in indicators (RSI, EMA, ATR); no future bars are referenced.
* calc\_on\_every\_tick = true for responsive visuals; Strategy Tester evaluates on bar close in history.
* Backtest stop/limit fills are simulated and may differ from live execution/liquidity.
DISCLAIMERS
* Educational use only. This is not financial advice. Markets involve risk. Past performance does not guarantee future results.
INPUTS (QUICK REFERENCE)
* rsiLen, rsiMaLen, rsiBuyThr, rsiSellThr
* emaTrendLen
* atrLen, atrMult, enableShort
* leftBars, rightBars, prec, showTable, maxYearsRows
SHORT TAGLINE
RSI momentum with 1R ATR brackets and a built-in Monthly/Yearly P\&L table.
TAGS
strategy, RSI, ATR, trend, risk-management, backtest, Pine-v4
Machine Learning BBPct [BackQuant]Machine Learning BBPct
What this is (in one line)
A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.
Background: %B in plain terms
Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes.
Why add (simplified) KNN?
Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas:
• Feature vector . Each bar is summarized by up to five normalized features:
– %B itself (normalized)
– Band width (volatility proxy)
– Price momentum (ROC)
– Volume momentum (ROC of volume)
– Price position within the bands
• Distance metric . Euclidean distance ranks the most similar recent bars.
• Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance.
• Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence.
This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration.
How the script is organized (by input groups)
1) BBPct Settings
• Price Source – Which price to evaluate (%B is computed from this).
• Calculation Period – Lookback for SMA basis and standard deviation.
• Multiplier – Standard deviation width (e.g., 2.0).
• Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B.
2) Thresholds
• Overbought/Oversold – Default 0.8 / 0.2 (inside ).
• Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions.
3) KNN Machine Learning
• Enable KNN – Switch between pure %B and hybrid.
• K (neighbors) – How many historical analogs to blend (default 8).
• Historical Period – Size of the search window for neighbors.
• ML Weight – Blend between raw %B and KNN estimate.
• Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows.
4) Filtering
• Method – None, Adaptive, Kalman-style (first-order),
or Hull smoothing.
• Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate.
5) Performance Tracking
• Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest).
• Early Entry Lookback – Horizon for forecasting a potential threshold cross.
• Profit Target / Stop Loss – Used only by the internal win-rate heuristic.
6) Self-Optimization
• Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes).
• Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is.
• Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3).
7) UI Settings
• Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays.
• Signal Line Width, Candle Painting, Colors – Visual preferences.
Step-by-step logic
A) Compute %B
Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev.
%B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB .
B) Build the feature vector
All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5).
C) Find nearest neighbors
For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K .
D) Predict and blend
Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient.
E) Adaptive thresholds
Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes.
F) Early entry heuristic
A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself.
G) Informational win-rate
The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest.
Outputs and visual language
• ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering.
• Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline.
• Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes.
• ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7.
• Early arrows – Optional small triangles for approaching OB/OS.
• Candle painting – Light green above the midline, light red below (optional).
• Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed.
Signal classification (informational)
The indicator does not fire trade commands; it labels state:
• STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds.
• BUY / SELL – finalBB beyond adaptive OS/OB.
• EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence.
• NEUTRAL – between adaptive bands.
Alerts (what you can automate)
• Entering adaptive OB/OS and extreme OB/OS.
• Midline cross (0.5).
• Overfitting detected (frequent parameter flipping).
• Early signals when early confidence > 0.7.
These are purely descriptive triggers around the indicator’s state.
Practical interpretation
• Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B.
• Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts.
• Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag.
• Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools.
• Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop.
Methodological notes
• Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that.
• Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts.
• Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias.
• Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA.
• Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator.
• Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative.
Limitations and cautions
• Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks).
• Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare.
• Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail.
• Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest.
• Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives.
Tuning guide
• Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength.
• Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest.
• Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges.
• Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes.
How to use it responsibly
Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation.
Summary
Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
