TEMA OBOS Strategy PakunTEMA OBOS Strategy
Overview
This strategy combines a trend-following approach using the Triple Exponential Moving Average (TEMA) with Overbought/Oversold (OBOS) indicator filtering.
By utilizing TEMA crossovers to determine trend direction and OBOS as a filter, it aims to improve entry precision.
This strategy can be applied to markets such as Forex, Stocks, and Crypto, and is particularly designed for mid-term timeframes (5-minute to 1-hour charts).
Strategy Objectives
Identify trend direction using TEMA
Use OBOS to filter out overbought/oversold conditions
Implement ATR-based dynamic risk management
Key Features
1. Trend Analysis Using TEMA
Uses crossover of short-term EMA (ema3) and long-term EMA (ema4) to determine entries.
ema4 acts as the primary trend filter.
2. Overbought/Oversold (OBOS) Filtering
Long Entry Condition: up > down (bullish trend confirmed)
Short Entry Condition: up < down (bearish trend confirmed)
Reduces unnecessary trades by filtering extreme market conditions.
3. ATR-Based Take Profit (TP) & Stop Loss (SL)
Adjustable ATR multiplier for TP/SL
Default settings:
TP = ATR × 5
SL = ATR × 2
Fully customizable risk parameters.
4. Customizable Parameters
TEMA Length (for trend calculation)
OBOS Length (for overbought/oversold detection)
Take Profit Multiplier
Stop Loss Multiplier
EMA Display (Enable/Disable TEMA lines)
Bar Color Change (Enable/Disable candle coloring)
Trading Rules
Long Entry (Buy Entry)
ema3 crosses above ema4 (Golden Cross)
OBOS indicator confirms up > down (bullish trend)
Execute a buy position
Short Entry (Sell Entry)
ema3 crosses below ema4 (Death Cross)
OBOS indicator confirms up < down (bearish trend)
Execute a sell position
Take Profit (TP)
Entry Price + (ATR × TP Multiplier) (Default: 5)
Stop Loss (SL)
Entry Price - (ATR × SL Multiplier) (Default: 2)
TP/SL settings are fully customizable to fine-tune risk management.
Risk Management Parameters
This strategy emphasizes proper position sizing and risk control to balance risk and return.
Trading Parameters & Considerations
Initial Account Balance: $7,000 (adjustable)
Base Currency: USD
Order Size: 10,000 USD
Pyramiding: 1
Trading Fees: $0.94 per trade
Long Position Margin: 50%
Short Position Margin: 50%
Total Trades (M5 Timeframe): 128
Deep Test Results (2024/11/01 - 2025/02/24)BTCUSD-5M
Total P&L：+1638.20USD
Max equity drawdown：694.78USD
Total trades：128
Profitable trades:44.53
Profit factor:1.45
These settings aim to protect capital while maintaining a balanced risk-reward approach.
Visual Support
TEMA Lines (Three EMAs)
Trend direction is indicated by color changes (Blue/Orange)
ema3 (short-term) and ema4 (long-term) crossover signals potential entries
OBOS Histogram
Green → Strong buying pressure
Red → Strong selling pressure
Blue → Possible trend reversal
Entry & Exit Markers
Blue Arrow → Long Entry Signal
Red Arrow → Short Entry Signal
Take Profit / Stop Loss levels displayed
Strategy Improvements & Uniqueness
This strategy is based on indicators developed by "l_lonthoff" and "jdmonto0", but has been significantly optimized for better entry accuracy, visual clarity, and risk management.
Enhanced Trend Identification with TEMA
Detects early trend reversals using ema3 & ema4 crossover
Reduces market noise for a smoother trend-following approach
Improved OBOS Filtering
Prevents excessive trading
Reduces unnecessary risk exposure
Dynamic Risk Management with ATR-Based TP/SL
Not a fixed value → TP/SL adjusts to market volatility
Fully customizable ATR multiplier settings
(Default: TP = ATR × 5, SL = ATR × 2)
Summary
The TEMA + OBOS Strategy is a simple yet powerful trading method that integrates trend analysis and oscillators.
TEMA for trend identification
OBOS for noise reduction & overbought/oversold filtering
ATR-based TP/SL settings for dynamic risk management
Before using this strategy, ensure thorough backtesting and demo trading to fine-tune parameters according to your trading style.
Cari dalam skrip untuk "entry"
Enhanced KLSE Banker Flow Oscillator# Enhanced KLSE Banker Flow Oscillator
## Description
The Enhanced KLSE Banker Flow Oscillator is a sophisticated technical analysis tool designed specifically for the Malaysian stock market (KLSE). This indicator analyzes price and volume relationships to identify potential smart money movements, providing early signals for market reversals and continuation patterns.
The oscillator measures the buying and selling pressure in the market with a focus on detecting institutional activity. By combining money flow calculations with volume filters and price action analysis, it helps traders identify high-probability trading opportunities with reduced noise.
## Key Features
- Dual-Timeframe Analysis: Combines long-term money flow trends with short-term momentum shifts for more accurate signals
- Adaptive Volume Filtering: Automatically adjusts volume thresholds based on recent market conditions
- Advanced Divergence Detection: Identifies potential trend reversals through price-flow divergences
- Early Signal Detection: Provides anticipatory signals before major price movements occur
- Multiple Signal Types: Offers both early alerts and strong confirmation signals with clear visual markers
- Volatility Adjustment: Adapts sensitivity based on current market volatility for more reliable signals
- Comprehensive Visual Feedback: Color-coded oscillator, signal markers, and optional text labels
- Customizable Display Options: Toggle momentum histogram, early signals, and zone fills
- Organized Settings Interface: Logically grouped parameters for easier configuration
## Indicator Components
1. Main Oscillator Line: The primary banker flow line that fluctuates above and below zero
2. Early Signal Line: Secondary indicator showing potential emerging signals
3. Momentum Histogram: Visual representation of flow momentum changes
4. Zone Fills: Color-coded background highlighting positive and negative zones
5. Signal Markers: Visual indicators for entry and exit points
6. Reference Lines: Key levels for strong and early signals
7. Signal Labels: Optional text annotations for significant signals
## Signal Types
1. Strong Buy Signal (Green Arrow): Major bullish signal with high probability of success
2. Strong Sell Signal (Red Arrow): Major bearish signal with high probability of success
3. Early Buy Signal (Blue Circle): First indication of potential bullish trend
4. Early Sell Signal (Red Circle): First indication of potential bearish trend
5. Bullish Divergence (Yellow Triangle Up): Price making lower lows while flow makes higher lows
6. Bearish Divergence (Yellow Triangle Down): Price making higher highs while flow makes lower highs
## Parameters Explained
### Core Settings
- MFI Base Length (14): Primary calculation period for money flow index
- Short-term Flow Length (5): Calculation period for early signals
- KLSE Sensitivity (1.8): Multiplier for flow calculations, higher = more sensitive
- Smoothing Length (5): Smoothing period for the main oscillator line
### Volume Filter Settings
- Volume Filter % (65): Minimum volume threshold as percentage of average
- Use Adaptive Volume Filter (true): Dynamically adjusts volume thresholds
### Signal Levels
- Strong Signal Level (15): Threshold for strong buy/sell signals
- Early Signal Level (10): Threshold for early buy/sell signals
- Early Signal Threshold (0.75): Sensitivity factor for early signals
### Advanced Settings
- Divergence Lookback (34): Period for checking price-flow divergences
- Show Signal Labels (true): Toggle text labels for signals
### Visual Settings
- Show Momentum Histogram (true): Toggle the momentum histogram display
- Show Early Signal (true): Toggle the early signal line display
- Show Zone Fills (true): Toggle background color fills
## How to Use This Indicator
### Installation
1. Add the indicator to your TradingView chart
2. Default settings are optimized for KLSE stocks
3. Customize parameters if needed for specific stocks
### Basic Interpretation
- Oscillator Above Zero: Bullish bias, buying pressure dominates
- Oscillator Below Zero: Bearish bias, selling pressure dominates
- Crossing Zero Line: Potential shift in market sentiment
- Extreme Readings: Possible overbought/oversold conditions
### Advanced Interpretation
- Divergences: Early warning of trend exhaustion
- Signal Confluences: Multiple signal types appearing together increase reliability
- Volume Confirmation: Signals with higher volume are more significant
- Momentum Alignment: Histogram should confirm direction of main oscillator
### Trading Strategies
#### Trend Following Strategy
1. Identify market trend direction
2. Wait for pullbacks shown by oscillator moving against trend
3. Enter when oscillator reverses back in trend direction with a Strong signal
4. Place stop loss below/above recent swing low/high
5. Take profit at previous resistance/support levels
#### Counter-Trend Strategy
1. Look for oscillator reaching extreme levels
2. Identify divergence between price and oscillator
3. Wait for oscillator to cross Early signal threshold
4. Enter position against prevailing trend
5. Use tight stop loss (1 ATR from entry)
6. Take profit at first resistance/support level
#### Breakout Confirmation Strategy
1. Identify stock consolidating in a range
2. Wait for price to break out of range
3. Confirm breakout with oscillator crossing zero line in breakout direction
4. Enter position in breakout direction
5. Place stop loss below/above the breakout level
6. Trail stop as price advances
### Signal Hierarchy and Reliability
From highest to lowest reliability:
1. Strong Buy/Sell signals with divergence and high volume
2. Strong Buy/Sell signals with high volume
3. Divergence signals followed by Early signals
4. Strong Buy/Sell signals with normal volume
5. Early Buy/Sell signals with high volume
6. Early Buy/Sell signals with normal volume
## Complete Trading Plan Example
### KLSE Market Trading System
#### Pre-Trading Preparation
1. Review overall market sentiment (bullish, bearish, or neutral)
2. Scan for stocks showing significant banker flow signals
3. Note key support/resistance levels for watchlist stocks
4. Prioritize trade candidates based on signal strength and volume
#### Entry Rules for Long Positions
1. Banker Flow Oscillator above zero line (positive flow environment)
2. One or more of the following signals present:
- Strong Buy signal (green arrow)
- Bullish Divergence signal (yellow triangle up)
- Early Buy signal (blue circle) with confirming price action
3. Entry confirmation requirements:
- Volume above 65% of 20-day average
- Price above short-term moving average (e.g., 20 EMA)
- No immediate resistance within 3% of entry price
4. Entry on the next candle open after signal confirmation
#### Entry Rules for Short Positions
1. Banker Flow Oscillator below zero line (negative flow environment)
2. One or more of the following signals present:
- Strong Sell signal (red arrow)
- Bearish Divergence signal (yellow triangle down)
- Early Sell signal (red circle) with confirming price action
3. Entry confirmation requirements:
- Volume above 65% of 20-day average
- Price below short-term moving average (e.g., 20 EMA)
- No immediate support within 3% of entry price
4. Entry on the next candle open after signal confirmation
#### Position Sizing Rules
1. Base risk per trade: 1% of trading capital
2. Position size calculation: Capital × Risk% ÷ Stop Loss Distance
3. Position size adjustments:
- Increase by 20% for Strong signals with above-average volume
- Decrease by 20% for Early signals without confirming price action
- Standard size for all other valid signals
#### Stop Loss Placement
1. For Long Positions:
- Place stop below the most recent swing low
- Minimum distance: 1.5 × ATR(14)
- Maximum risk: 1% of trading capital
2. For Short Positions:
- Place stop above the most recent swing high
- Minimum distance: 1.5 × ATR(14)
- Maximum risk: 1% of trading capital
#### Take Profit Strategy
1. First Target (33% of position):
- 1.5:1 reward-to-risk ratio
- Move stop to breakeven after reaching first target
2. Second Target (33% of position):
- 2.5:1 reward-to-risk ratio
- Trail stop at previous day's low/high
3. Final Target (34% of position):
- 4:1 reward-to-risk ratio or
- Exit when opposing signal appears (e.g., Strong Sell for long positions)
#### Trade Management Rules
1. After reaching first target:
- Move stop to breakeven
- Consider adding to position if new confirming signal appears
2. After reaching second target:
- Trail stop using banker flow signals
- Exit remaining position when:
- Oscillator crosses zero line in opposite direction
- Opposing signal appears
- Price closes below/above trailing stop level
3. Maximum holding period:
- 20 trading days for trend-following trades
- 10 trading days for counter-trend trades
- Re-evaluate if targets not reached within timeframe
#### Risk Management Safeguards
1. Maximum open positions: 5 trades
2. Maximum sector exposure: 40% of trading capital
3. Maximum daily drawdown limit: 3% of trading capital
4. Mandatory stop trading rules:
- After three consecutive losing trades
- After reaching 5% account drawdown
- Resume after two-day cooling period and strategy review
#### Performance Tracking
1. Track for each trade:
- Signal type that triggered entry
- Oscillator reading at entry and exit
- Volume relative to average
- Price action confirmation patterns
- Holding period
- Reward-to-risk achieved
2. Review performance metrics weekly:
- Win rate by signal type
- Average reward-to-risk ratio
- Profit factor
- Maximum drawdown
3. Adjust strategy parameters based on performance:
- Increase position size for highest performing signals
- Decrease or eliminate trades based on underperforming signals
## Advanced Usage Tips
1. Combine with Support/Resistance:
- Signals are more reliable when they occur at key support/resistance levels
- Look for banker flow divergence at major price levels
2. Multiple Timeframe Analysis:
- Use the oscillator on both daily and weekly timeframes
- Stronger signals when both timeframes align
- Enter on shorter timeframe when confirmed by longer timeframe
3. Sector Rotation Strategy:
- Compare banker flow across different sectors
- Rotate capital to sectors showing strongest positive flow
- Avoid sectors with persistent negative flow
4. Volatility Adjustments:
- During high volatility periods, wait for Strong signals only
- During low volatility periods, Early signals can be more actionable
5. Optimizing Parameters:
- For more volatile stocks: Increase Smoothing Length (6-8)
- For less volatile stocks: Decrease KLSE Sensitivity (1.2-1.5)
- For intraday trading: Reduce all length parameters by 30-50%
## Fine-Tuning for Different Markets
While optimized for KLSE, the indicator can be adapted for other markets:
1. For US Stocks:
- Reduce KLSE Sensitivity to 1.5
- Increase Volume Filter to 75%
- Adjust Strong Signal Level to 18
2. For Forex:
- Increase Smoothing Length to 8
- Reduce Early Signal Threshold to 0.6
- Focus more on divergence signals than crossovers
3. For Cryptocurrencies:
- Increase KLSE Sensitivity to 2.2
- Reduce Signal Levels (Strong: 12, Early: 8)
- Use higher Volume Filter (80%)
By thoroughly understanding and properly implementing the Enhanced KLSE Banker Flow Oscillator, traders can gain a significant edge in identifying institutional money flow and making more informed trading decisions, particularly in the Malaysian stock market.
SuperTrend AI Oscillator StrategySuperTrend AI Oscillator Strategy
Overview
This strategy is a trend-following approach that combines the SuperTrend indicator with oscillator-based filtering.
By identifying market trends while utilizing oscillator-based momentum analysis, it aims to improve entry precision.
Additionally, it incorporates a trailing stop to strengthen risk management while maximizing profits.
This strategy can be applied to various markets, including Forex, Crypto, and Stocks, as well as different timeframes. However, its effectiveness varies depending on market conditions, so thorough testing is required.
Features
1️⃣ Trend Identification Using SuperTrend
The SuperTrend indicator (a volatility-adjusted trend indicator based on ATR) is used to determine trend direction.
A long entry is considered when SuperTrend turns bullish.
A short entry is considered when SuperTrend turns bearish.
The goal is to capture clear trend reversals and avoid unnecessary trades in ranging markets.
2️⃣ Entry Filtering with an Oscillator
The Super Oscillator is used to filter entry signals.
If the oscillator exceeds 50, it strengthens long entries (indicating strong bullish momentum).
If the oscillator drops below 50, it strengthens short entries (indicating strong bearish momentum).
This filter helps reduce trades in uncertain market conditions and improves entry accuracy.
3️⃣ Risk Management with a Trailing Stop
Instead of a fixed stop loss, a SuperTrend-based trailing stop is implemented.
The stop level adjusts automatically based on market volatility.
This allows profits to run while managing downside risk effectively.
4️⃣ Adjustable Risk-Reward Ratio
The default risk-reward ratio is set at 1:2.
Example: A 1% stop loss corresponds to a 2% take profit target.
The ratio can be customized according to the trader’s risk tolerance.
5️⃣ Clear Trade Signals & Visual Support
Green "BUY" labels indicate long entry signals.
Red "SELL" labels indicate short entry signals.
The Super Oscillator is plotted in a separate subwindow to visually assess trend strength.
A real-time trailing stop is displayed to support exit strategies.
These visual aids make it easier to identify entry and exit points.
Trading Parameters & Considerations
Initial Account Balance: Default is $7,000 (adjustable).
Base Currency: USD
Order Size: 10,000 USD
Pyramiding: 1
Trading Fees: $0.94 per trade
Long Position Margin: 50%
Short Position Margin: 50%
Total Trades (M5 Timeframe): 1,032
Visual Aids for Clarity
This strategy includes clear visual trade signals to enhance decision-making:
Green "BUY" labels for long entries
Red "SELL" labels for short entries
Super Oscillator plotted in a subwindow with a 50 midline
Dynamic trailing stop displayed for real-time trend tracking
These visual aids allow traders to quickly identify trade setups and manage positions with greater confidence.
Summary
The SuperTrend AI Oscillator Strategy is developed based on indicators from Black Cat and LuxAlgo.
By integrating high-precision trend analysis with AI-based oscillator filtering, it provides a strong risk-managed trading approach.
Important Notes
This strategy does not guarantee profits—performance varies based on market conditions.
Past performance does not guarantee future results. Markets are constantly changing.
Always test extensively with backtesting and demo trading before using it in live markets.
Risk management, position sizing, and market conditions should always be considered when trading.
Conclusion
This strategy combines trend analysis with momentum filtering, enhancing risk management in trading.
By following market trends carefully, making precise entries, and using trailing stops, it seeks to reduce risk while maximizing potential profits.
Before using this strategy, be sure to test it thoroughly via backtesting and demo trading, and adjust the settings to match your trading style.
Aggressive Strategy for High IV Market### Strategic background
In a volatile high IV market, prices are volatile and market expectations of future uncertainty are high. This environment provides opportunities for aggressive trading strategies, but also comes with a high level of risk. In pursuit of a high Sharpe ratio (i.e., risk-adjusted return), we need to design a strategy that captures the benefits of market volatility while effectively controlling risk. Based on daily line cycles, I choose a combination of trend tracking and volatility filtering for highly volatile assets such as stocks, futures or cryptocurrencies.
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### Strategy framework
#### Data
- Use daily data, including opening, closing, high and low prices.
- Suitable for highly volatile markets such as technology stocks, cryptocurrencies or volatile index futures.
#### Core indicators
1. ** Trend Indicators ** :
Fast Exponential Moving Average (EMA_fast) : 10-day EMA, used to capture short-term trends.
- Slow Exponential Moving Average (EMA_slow) : 30-day EMA, used to determine the long-term trend.
2. ** Volatility Indicators ** :
Average true Volatility (ATR) : 14-day ATR, used to measure market volatility.
- ATR mean (ATR_mean) : A simple moving average of the 20-day ATR that serves as a volatility benchmark.
- ATR standard deviation (ATR_std) : The standard deviation of the 20-day ATR, which is used to judge extreme changes in volatility.
#### Trading logic
The strategy is based on a trend following approach of double moving averages and filters volatility through ATR indicators, ensuring that trading only in a high-volatility environment is in line with aggressive and high sharpe ratio goals.
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### Entry and exit conditions
#### Admission conditions
- ** Multiple entry ** :
- EMA_fast Crosses EMA_slow (gold cross), indicating that the short-term trend is turning upward.
-ATR > ATR_mean + 1 * ATR_std indicates that the current volatility is above average and the market is in a state of high volatility.
- ** Short Entry ** :
- EMA_fast Crosses EMA_slow (dead cross) downward, indicating that the short-term trend turns downward.
-ATR > ATR_mean + 1 * ATR_std, confirming high volatility.
#### Appearance conditions
- ** Long show ** :
- EMA_fast Enters the EMA_slow (dead cross) downward, and the trend reverses.
- or ATR < ATR_mean-1 * ATR_std, volatility decreases significantly and the market calms down.
- ** Bear out ** :
- EMA_fast Crosses the EMA_slow (gold cross) on the top, and the trend reverses.
- or ATR < ATR_mean-1 * ATR_std, the volatility is reduced.
---
### Risk management
To control the high risk associated with aggressive strategies, set up the following mechanisms:
1. ** Stop loss ** :
- Long: Entry price - 2 * ATR.
- Short: Entry price + 2 * ATR.
- Dynamic stop loss based on ATR can adapt to market volatility changes.
2. ** Stop profit ** :
- Fixed profit target can be selected (e.g. entry price ± 4 * ATR).
- Or use trailing stop losses to lock in profits following price movements.
3. ** Location Management ** :
- Reduce positions appropriately in times of high volatility, such as dynamically adjusting position size according to ATR, ensuring that the risk of a single trade does not exceed 1%-2% of the account capital.
---
### Strategy features
- ** Aggressiveness ** : By trading only in a high ATR environment, the strategy takes full advantage of market volatility and pursues greater returns.
- ** High Sharpe ratio potential ** : Trend tracking combined with volatility filtering to avoid ineffective trades during periods of low volatility and improve the ratio of return to risk.
- ** Daily line Cycle ** : Based on daily line data, suitable for traders who operate frequently but are not too complex.
---
### Implementation steps
1. ** Data Preparation ** :
- Get the daily data of the target asset.
- Calculate EMA_fast (10 days), EMA_slow (30 days), ATR (14 days), ATR_mean (20 days), and ATR_std (20 days).
2. ** Signal generation ** :
- Check EMA cross signals and ATR conditions daily to generate long/short signals.
3. ** Execute trades ** :
- Enter according to the signal, set stop loss and profit.
- Monitor exit conditions and close positions in time.
4. ** Backtest and Optimization ** :
- Use historical data to backtest strategies to evaluate Sharpe ratios, maximum retracements, and win rates.
- Optimize parameters such as EMA period and ATR threshold to improve policy performance.
---
### Precautions
- ** Trading costs ** : Highly volatile markets may result in frequent trading, and the impact of fees and slippage on earnings needs to be considered.
- ** Risk Control ** : Aggressive strategies may face large retracements and need to strictly implement stop losses.
- ** Scalability ** : Additional metrics (such as volume or VIX) can be added to enhance strategy robustness, or combined with machine learning to predict trends and volatility.
---
### Summary
This is a trend following strategy based on dual moving averages and ATR, designed for volatile high IV markets. By entering into high volatility and exiting into low volatility, the strategy combines aggressive and risk-adjusted returns for traders seeking a high sharpe ratio. It is recommended to fully backtest before implementation and adjust the parameters according to the specific market.
ADX for BTC [PineIndicators]The ADX Strategy for BTC is a trend-following system that uses the Average Directional Index (ADX) to determine market strength and momentum shifts. Designed for Bitcoin trading, this strategy applies a customizable ADX threshold to confirm trend signals and optionally filters entries using a Simple Moving Average (SMA). The system features automated entry and exit conditions, dynamic trade visualization, and built-in trade tracking for historical performance analysis.
⚙️ Core Strategy Components
1️⃣ Average Directional Index (ADX) Calculation
The ADX indicator measures trend strength without indicating direction. It is derived from the Positive Directional Movement (+DI) and Negative Directional Movement (-DI):
+DI (Positive Directional Index): Measures upward price movement.
-DI (Negative Directional Index): Measures downward price movement.
ADX Value: Higher values indicate stronger trends, regardless of direction.
This strategy uses a default ADX length of 14 to smooth out short-term fluctuations while detecting sustainable trends.
2️⃣ SMA Filter (Optional Trend Confirmation)
The strategy includes a 200-period SMA filter to validate trend direction before entering trades. If enabled:
✅ Long Entry is only allowed when price is above a long-term SMA multiplier (5x the standard SMA length).
✅ If disabled, the strategy only considers the ADX crossover threshold for trade entries.
This filter helps reduce entries in sideways or weak-trend conditions, improving signal reliability.
📌 Trade Logic & Conditions
🔹 Long Entry Conditions
A buy signal is triggered when:
✅ ADX crosses above the threshold (default = 14), indicating a strengthening trend.
✅ (If SMA filter is enabled) Price is above the long-term SMA multiplier.
🔻 Exit Conditions
A position is closed when:
✅ ADX crosses below the stop threshold (default = 45), signaling trend weakening.
By adjusting the entry and exit ADX levels, traders can fine-tune sensitivity to trend changes.
📏 Trade Visualization & Tracking
Trade Markers
"Buy" label (▲) appears when a long position is opened.
"Close" label (▼) appears when a position is exited.
Trade History Boxes
Green if a trade is profitable.
Red if a trade closes at a loss.
Trend Tracking Lines
Horizontal lines mark entry and exit prices.
A filled trade box visually represents trade duration and profitability.
These elements provide clear visual insights into trade execution and performance.
⚡ How to Use This Strategy
1️⃣ Apply the script to a BTC chart in TradingView.
2️⃣ Adjust ADX entry/exit levels based on trend sensitivity.
3️⃣ Enable or disable the SMA filter for trend confirmation.
4️⃣ Backtest performance to analyze historical trade execution.
5️⃣ Monitor trade markers and history boxes for real-time trend insights.
This strategy is designed for trend traders looking to capture high-momentum market conditions while filtering out weak trends.
Bot for Spot Market - Custom GridThis script is designed to create a trading bot for the spot market, specifically for buying and selling bitcoins profitably. Recommended for timeframes above two hours. Here are the main functions and features of the script:
Strategy Setup: The bot is set up with a custom grid strategy, defining parameters like pyramiding (allowed number of simultaneous trades), margin requirements, commission, and initial capital.
Order Requirements: It calculates the order price and amount based on the minimum requirements set by the exchange and rounds them appropriately.
Entry Conditions: The bot makes new entries if the closing price falls a certain percentage below the last entry price. It continues to make entries until the closing price rises a certain percentage above the average entry price.
Targets and Plots:
It calculates and plots the target profit level.
It plots the average entry price and the last entry price.
It plots the next entry price based on the defined conditions.
It plots the maximum number of orders allowed based on equity and the number of open orders.
Timerange: The bot can start trading from a specific date and time defined by the user.
Entries: It places orders if the timerange conditions are met. It also places new orders if the closing price is below the last entry price by a defined percentage.
Profit Calculation: The script calculates open profit or loss for the open positions.
Exit Conditions: It closes all positions if the open profit is positive and the closing price is above the target profit level.
Performance Table: The bot maintains and displays statistics like the number of open and closed trades, net profit, and equity in a table format.
The script is customizable, allowing users to adjust parameters like initial capital, commission, order values, and profit targets to fit their specific trading needs and exchange requirements.
Supertrend Strategy with Money Ocean TradeStrategy Overview
The Supertrend Strategy with Trend Change Confirmation leverages the Supertrend indicator to identify potential buy and sell signals based on changes in trend direction and subsequent price action. The strategy is designed to work with any financial instrument (symbol) and aims to provide clear entry and exit signals.
Key Components
Supertrend Indicator: The core of this strategy is the Supertrend indicator, calculated using a length of 3 and a factor of 1. The Supertrend line is plotted on the chart to visually represent trend direction.
Direction 1: Indicates an uptrend (bullish).
Direction -1: Indicates a downtrend (bearish).
Trend Change Detection: The strategy monitors changes in the trend direction. When a trend change is detected, it checks if the next candle confirms the trend change by breaking above or below the Supertrend line.
Entry Conditions:
Long Entry (Buy): When the Supertrend direction changes to 1 (uptrend) and the next candle closes above the Supertrend line.
Short Entry (Sell): When the Supertrend direction changes to -1 (downtrend) and the next candle closes below the Supertrend line.
Exit Conditions: The strategy closes the position based on the opposite signal.
Long Exit: When the Supertrend direction changes to -1 (downtrend) and the next candle closes below the Supertrend line.
Short Exit: When the Supertrend direction changes to 1 (uptrend) and the next candle closes above the Supertrend line.
Visual Signals: The strategy plots buy and sell signals on the chart using plotshape:
BUY: A green label below the bar when a long entry is triggered.
SELL: A red label above the bar when a short entry is triggered.
Alerts: Alerts are set up to notify when a buy or sell signal is triggered.
Script Summary
This strategy helps traders identify potential trading opportunities based on trend changes and confirms the trend by checking the next candle's price action. The visual signals and dashboard enhance the user's ability to monitor and manage trades effectively.
Feel free to test and adjust the parameters to suit your trading preferences! If you need further customizations or explanations, let me know.
[3Commas] HA & MAHA & MA
🔷What it does: This tool is designed to test a trend-following strategy using Heikin Ashi candles and moving averages. It enters trades after pullbacks, aiming to let profits run once the risk-to-reward ratio reaches 1:1 while securing the position.
🔷Who is it for: It is ideal for traders looking to compare final results using fixed versus dynamic take profits by adjusting parameters and trade direction—a concept applicable to most trading strategies.
🔷How does it work: We use moving averages to define the market trend, then wait for opposite Heikin Ashi candles to form against it. Once these candles reverse in favor of the trend, we enter the trade, using the last swing created by the pullback as the stop loss. By applying the breakeven ratio, we protect the trade and let it run, using the slower moving average as a trailing stop.
A buy signal is generated when:
The previous candle is bearish (ha_bear ), indicating a pullback.
The fast moving average (ma1) is above the slow moving average (ma2), confirming an uptrend.
The current candle is bullish (ha_bull), showing trend continuation.
The Heikin Ashi close is above the fast moving average (ma1), reinforcing the bullish bias.
The real price close is above the open (close > open), ensuring bullish momentum in actual price data.
The signal is confirmed on the closed candle (barstate.isconfirmed) to avoid premature signals.
dir is undefined (na(dir)), preventing repeated signals in the same direction.
A sell signal is generated when:
The previous candle is bullish (ha_bull ), indicating a temporary upward move before a potential reversal.
The fast moving average (ma1) is below the slow moving average (ma2), confirming a downtrend.
The current candle is bearish (ha_bear), showing trend continuation to the downside.
The Heikin Ashi close is below the fast moving average (ma1), reinforcing bearish pressure.
The real price close is below the open (close < open), confirming bearish momentum in actual price data.
The signal is confirmed after the candle closes (barstate.isconfirmed), avoiding premature entries.
dir is undefined (na(dir)), preventing consecutive signals in the same direction.
In simple terms, this setup looks for trend continuation after a pullback, confirming entries with both Heikin Ashi and real price action, supported by moving average alignment to avoid false signals.
If the price reaches a 1:1 risk-to-reward ratio, the stop will be moved to the entry point. However, if the slow moving average surpasses this level, it will become the new exit point, acting as a trailing stop
🔷Why It’s Unique
Easily visualizes the benefits of using risk-to-reward ratios when trading instead of fixed percentages.
Provides a simple and straightforward approach to trading, embracing the "keep it simple" concept.
Offers clear visualization of DCA Bot entry and exit points based on user preferences.
Includes an option to review the message format before sending signals to bots, with compatibility for multi-pair and futures contract pairs.
🔷 Considerations Before Using the Indicator
⚠️Very important: The indicator must be used on charts with real price data, such as Japanese candlesticks, line charts, etc. Do not use it on Heikin Ashi charts, as this may lead to unrealistic results.
🔸Since this is a trend-following strategy, use it on timeframes above 4 hours, where market noise is reduced and trends are clearer. Also, carefully review the statistics before using it, focusing on pairs that tend to have long periods of well-defined trends.
🔸Disadvantages:
False Signals in Ranges: Consolidating markets can generate unreliable signals.
Lagging Indicator: Being based on moving averages, it may react late to sudden price movements.
🔸Advantages:
Trend Focused: Simplifies the identification of trending markets.
Noise Reduction: Uses Heikin Ashi candles to identify trend continuation after pullbacks.
Broad Applicability: Suitable for forex, crypto, stocks, and commodities.
🔸The strategy provides a systematic way to analyze markets but does not guarantee successful outcomes. Use it as an additional tool rather than relying solely on an automated system.
Trading results depend on various factors, including market conditions, trader discipline, and risk management. Past performance does not ensure future success, so always approach the market cautiously.
🔸Risk Management: Define stop-loss levels, position sizes, and profit targets before entering any trade. Be prepared for potential losses and ensure your approach aligns with your overall trading plan.
🔷 STRATEGY PROPERTIES
Symbol: BINANCE:BTCUSDT (Spot).
Timeframe: 4h.
Test Period: All historical data available.
Initial Capital: 10000 USDT.
Order Size per Trade: 1% of Capital, you can use a higher value e.g. 5%, be cautious that the Max Drawdown does not exceed 10%, as it would indicate a very risky trading approach.
Commission: Binance commission 0.1%, adjust according to the exchange being used, lower numbers will generate unrealistic results. By using low values e.g. 5%, it allows us to adapt over time and check the functioning of the strategy.
Slippage: 5 ticks, for pairs with low liquidity or very large orders, this number should be increased as the order may not be filled at the desired level.
Margin for Long and Short Positions: 100%.
Indicator Settings: Default Configuration.
MA1 Length: 9.
MA2 Length: 18.
MA Calculations: EMA.
Take Profit Ratio: Disable. Ratio 1:4.
Breakeven Ratio: Enable, Ratio 1:1.
Strategy: Long & Short.
🔷 STRATEGY RESULTS
⚠️Remember, past results do not guarantee future performance.
Net Profit: +324.88 USDT (+3.25%).
Max Drawdown: -81.18 USDT (-0.78%).
Total Closed Trades: 672.
Percent Profitable: 35.57%.
Profit Factor: 1.347.
Average Trade: +0.48 USDT (+0.48%).
Average # Bars in Trades: 13.
🔷 HOW TO USE
🔸 Adjust Settings:
The default values—MA1 (9) and MA2 (18) with EMA calculation—generally work well. However, you can increase these values, such as 20 and 40, to better identify stronger trends.
🔸 Choose a Symbol that Typically Trends:
Select an asset that tends to form clear trends. Keep in mind that the Strategy Tester results may show poor performance for certain assets, making them less suitable for sending signals to bots.
🔸 Experiment with Ratios:
Test different take profit and breakeven ratios to compare various scenarios—especially to observe how the strategy performs when only the trade is protected.
🔸This is an example of how protecting the trade works: once the price moves in favor of the position with a 1:1 risk-to-reward ratio, the stop loss is moved to the entry price. If the Slow MA surpasses this level, it will act as a trailing stop, aiming to follow the trend and maximize potential gains.
🔸In contrast, in this example, for the same trade, if we set a take profit at a 1:3 risk-to-reward ratio—which is generally considered a good risk-reward relationship—we can see how a significant portion of the upward move is left on the table.
🔸Results Review:
It is important to check the Max Drawdown. This value should ideally not exceed 10% of your capital. Consider adjusting the trade size to ensure this threshold is not surpassed.
Remember to include the correct values for commission and slippage according to the symbol and exchange where you are conducting the tests. Otherwise, the results will not be realistic.
If you are satisfied with the results, you may consider automating your trades. However, it is strongly recommended to use a small amount of capital or a demo account to test proper execution before committing real funds.
🔸Create alerts to trigger the DCA Bot:
Verify Messages: Ensure the message matches the one specified by the DCA Bot.
Multi-Pair Configuration: For multi-pair setups, enable the option to add the symbol in the correct format.
Signal Settings: Enable whether you want to receive long or short signals (Entry | TP | SL), copy and paste the the messages for the DCA Bots configured.
Alert Setup:
When creating an alert, set the condition to the indicator and choose "alert() function call only.
Enter any desired Alert Name.
Open the Notifications tab, enable Webhook URL, and paste the Webhook URL.
For more details, refer to the section: "How to use TradingView Custom Signals".
Finalize Alerts: Click Create, you're done! Alerts will now be sent automatically in the correct format.
🔷 INDICATOR SETTINGS
MA 1: Fast MA Length
MA 2: Slow MA Length
MA Calc: MA's Calculations (SMA,EMA, RMA,WMA)
TP Ratio: This is the take profit ratio relative to the stop loss, where the trade will be closed in profit.
BE Ratio: This is the breakeven ratio relative to the stop loss, where the stop loss will be updated to breakeven or if the MA2 is greater than this level.
Strategy: Order Type direction in which trades are executed.
Use Custom Test Period: When enabled signals only works in the selected time window. If disabled it will use all historical data available on the chart.
Test Start and End: Once the Custom Test Period is enabled, here you select the start and end date that you want to analyze.
Check Messages: Enable the table to review the messages to be sent to the bot.
Entry | TP | SL: Enable this options to send Buy Entry, Take Profit (TP), and Stop Loss (SL) signals.
Deal Entry and Deal Exit : Copy and paste the message for the deal start signal and close order at Market Price of the DCA Bot. This is the message that will be sent with the alert to the Bot, you must verify that it is the same as the bot so that it can process properly so that it executes and starts the trade.
DCA Bot Multi-Pair: You must activate it if you want to use the signals in a DCA Bot Multi-pair in the text box you must enter (using the correct format) the symbol in which you are creating the alert, you can check the format of each symbol when you create the bot.
👨🏻💻💭 We hope this tool helps enhance your trading. Your feedback is invaluable, so feel free to share any suggestions for improvements or new features you'd like to see implemented.
__
The information and publications within the 3Commas TradingView account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc.
Smart MA Crossover BacktesterSmart MA Crossover Backtester - Strategy Overview
Strategy Name: Smart MA Crossover Backtester
Published on: TradingView
Applicable Markets: Works well on crypto (tested profitably on ETH)
Strategy Concept
The Smart MA Crossover Backtester is an improved Moving Average (MA) crossover strategy that incorporates a trend filter and an ATR-based stop loss & take profit mechanism for better risk management. It aims to capture trends efficiently while reducing false signals by only trading in the direction of the long-term trend.
Core Components & Logic
Moving Averages (MA) for Entry Signals
Fast Moving Average (9-period SMA)
Slow Moving Average (21-period SMA)
A trade signal is generated when the fast MA crosses the slow MA.
Trend Filter (200-period SMA)
Only enters long positions if price is above the 200-period SMA (bullish trend).
Only enters short positions if price is below the 200-period SMA (bearish trend).
This helps in avoiding counter-trend trades, reducing whipsaws.
ATR-Based Stop Loss & Take Profit
Uses the Average True Range (ATR) with a multiplier of 2 to calculate stop loss.
Risk-Reward Ratio = 1:2 (Take profit is set at 2x ATR).
This ensures dynamic stop loss and take profit levels based on market volatility.
Trading Rules
✅ Long Entry (Buy Signal):
Fast MA (9) crosses above Slow MA (21)
Price is above the 200 MA (bullish trend filter active)
Stop Loss: Below entry price by 2× ATR
Take Profit: Above entry price by 4× ATR
✅ Short Entry (Sell Signal):
Fast MA (9) crosses below Slow MA (21)
Price is below the 200 MA (bearish trend filter active)
Stop Loss: Above entry price by 2× ATR
Take Profit: Below entry price by 4× ATR
Why This Strategy Works Well for Crypto (ETH)?
🔹 Crypto markets are highly volatile – ATR-based stop loss adapts dynamically to market conditions.
🔹 Long-term trend filter (200 MA) ensures trading in the dominant direction, reducing false signals.
🔹 Risk-reward ratio of 1:2 allows for profitable trades even with a lower win rate.
This strategy has been tested on Ethereum (ETH) and has shown profitable performance, making it a strong choice for crypto traders looking for trend-following setups with solid risk management. 🚀
Stochastic-Dynamic Volatility Band ModelThe Stochastic-Dynamic Volatility Band Model is a quantitative trading approach that leverages statistical principles to model market volatility and generate buy and sell signals. The strategy is grounded in the concepts of volatility estimation and dynamic market regimes, where the core idea is to capture price fluctuations through stochastic models and trade around volatility bands.
Volatility Estimation and Band Construction
The volatility bands are constructed using a combination of historical price data and statistical measures, primarily the standard deviation (σ) of price returns, which quantifies the degree of variation in price movements over a specific period. This methodology is based on the classical works of Black-Scholes (1973), which laid the foundation for using volatility as a core component in financial models. Volatility is a crucial determinant of asset pricing and risk, and it plays a pivotal role in this strategy's design.
Entry and Exit Conditions
The entry conditions are based on the price’s relationship with the volatility bands. A long entry is triggered when the price crosses above the lower volatility band, indicating that the market may have been oversold or is experiencing a reversal to the upside. Conversely, a short entry is triggered when the price crosses below the upper volatility band, suggesting overbought conditions or a potential market downturn.
These entry signals are consistent with the mean reversion theory, which asserts that asset prices tend to revert to their long-term average after deviating from it. According to Poterba and Summers (1988), mean reversion occurs due to overreaction to news or temporary disturbances, leading to price corrections.
The exit condition is based on the number of bars that have elapsed since the entry signal. Specifically, positions are closed after a predefined number of bars, typically set to seven bars, reflecting a short-term trading horizon. This exit mechanism is in line with short-term momentum trading strategies discussed in literature, where traders capitalize on price movements within specific timeframes (Jegadeesh & Titman, 1993).
Market Adaptability
One of the key features of this strategy is its dynamic nature, as it adapts to the changing volatility environment. The volatility bands automatically adjust to market conditions, expanding in periods of high volatility and contracting when volatility decreases. This dynamic adjustment helps the strategy remain robust across different market regimes, as it is capable of identifying both trend-following and mean-reverting opportunities.
This dynamic adaptability is supported by the adaptive market hypothesis (Lo, 2004), which posits that market participants evolve their strategies in response to changing market conditions, akin to the adaptive nature of biological systems.
References:
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Bollinger, J. (1980). Bollinger on Bollinger Bands. Wiley.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Money Flow Index MTF + Alerts with Candle Opacity & LabelsHow to Use the Money Flow Index MTF + Alerts with Candle Opacity & Labels Indicator
Overview:
This indicator is designed to help you gauge the buying and selling pressure in a market by using the Money Flow Index (MFI). Unlike many momentum oscillators, the MFI incorporates both price and volume, providing a unique perspective on market activity. It is particularly useful when you want to visually assess potential overbought or oversold conditions.
Indicator Components:
Money Flow Index (MFI) Calculation:
The indicator computes the MFI using a user-defined look-back period (default is 14 bars). The MFI is scaled between 0 and 100, where values above 80 typically indicate overbought conditions and values below 20 suggest oversold conditions.
Multi-Timeframe (MTF) Capability:
You can choose to calculate the MFI using either the current chart’s timeframe or a custom timeframe (for example, a 4-hour chart). This flexibility allows you to compare longer-term money flow trends against your primary trading timeframe.
Candle Opacity Based on MFI:
The opacity of the candles on your chart is dynamically adjusted based on the current MFI reading. When the MFI is high (near 100), candles become more opaque; when the MFI is low (near 0), candles appear more transparent. This visual cue can help you quickly spot changes in market momentum.
Visual Labels for Overbought/Oversold Conditions:
When the MFI crosses into the overbought territory, a red label reading “Overbought” is displayed above the high of the bar. Similarly, when it crosses into the oversold territory, a green label reading “Oversold” is placed below the low of the bar. These labels provide an immediate visual alert to potential reversal points or areas of caution.
Alert Conditions:
The script also includes alert conditions for both overbought and oversold signals. You can set up TradingView alerts so that you are notified in real time when the indicator detects these conditions.
Theory Behind the Money Flow Index (MFI):
The Money Flow Index is a momentum oscillator that uses both price and volume to signal the strength behind price moves.
Overbought Conditions: When the MFI is above 80, it suggests that buying pressure is very strong and the asset might be due for a pullback or consolidation.
Oversold Conditions: Conversely, when the MFI falls below 20, selling pressure is high and the asset might be oversold, potentially priming it for a bounce.
Keep in mind that in strong trending markets, overbought or oversold readings can persist for extended periods, so the MFI should be used in conjunction with other technical analysis tools.
Position Management Guidance:
While the indicator is useful for spotting potential overbought and oversold conditions, it is not designed to serve as an automatic signal to completely close a position. Instead, you might consider using it as a guide for pyramiding—gradually adding to your position over several days rather than exiting all at once. This approach allows you to better manage risk by:
Scaling In or Out Gradually: Instead of making one large position change, you can add or reduce your position in increments as market conditions evolve.
Diversifying Risk: Pyramiding helps you avoid the pitfalls of trying to time the market perfectly on a single trade exit or entry.
How to Get Started:
Apply the Indicator:
Add the indicator to your TradingView chart. Adjust the input settings (length, oversold/overbought levels, and resolution) as needed for your trading style and the market you’re analyzing.
Watch the Candles:
Observe the dynamic opacity of your candles. A sudden change in opacity can be a sign that the underlying money flow is shifting.
Monitor the Labels:
Pay attention to the “Overbought” or “Oversold” labels that appear. Use these cues in combination with your broader analysis to decide if it might be a good time to add to or gradually exit your position.
Set Up Alerts:
Configure TradingView alerts based on the indicator’s alert conditions so that you are notified when the MFI reaches extreme levels.
Use as Part of a Broader Strategy:
Remember, no single indicator should dictate your entire trading decision. Combine MFI signals with other technical analysis, risk management rules, and market insights to guide your trades.
GWAP (Gamma Weighted Average Price)Gamma Weighted Average Price (GWAP) Indicator
The Gamma Weighted Average Price (GWAP) is a dynamic financial indicator that applies exponentially decaying weights to historical prices to calculate a weighted average. The method leverages the exponential decay function, controlled by a gamma factor, to prioritize recent price data while gradually diminishing the influence of older observations. This approach builds upon techniques commonly found in time-series analysis, including Exponentially Weighted Moving Averages (EWMA), which are extensively used in financial modeling (Campbell, Lo & MacKinlay, 1997).
Theoretical Context and Justification
The gamma-weighted approach follows principles similar to those in Exponentially Weighted Moving Averages (EWMA), often used in volatility modeling, where weights decay exponentially over time. The exponential decay model can improve signal responsiveness compared to simple moving averages (Hyndman & Athanasopoulos, 2018). This design helps capture recent market dynamics without ignoring past trends, a common requirement in high-frequency trading systems (Bandi & Russell, 2006).
Practical Applications
1. Trend Detection:
The GWAP can help identify bullish and bearish trends:
• When the price is above GWAP, the market exhibits bullish momentum.
• Conversely, when the price is below GWAP, bearish momentum prevails.
2. Volatility Filtering:
Because of the gamma weighting mechanism, GWAP reduces the noise commonly seen in volatile markets, making it a useful tool for traders looking to smooth price fluctuations while retaining actionable signals.
3. Crossovers for Trade Signals:
Similar to moving average strategies, traders can use price crossovers with the GWAP as trade signals:
• Buy Signal: When the price crosses above the GWAP.
• Sell Signal: When the price crosses below the GWAP.
4. Adaptive Gamma Weighting:
The gamma factor allows for further customization.
• Higher gamma values (>1) place greater emphasis on older data, suitable for long-term trend analysis.
• Lower gamma values (<1) heavily weight recent price movements, ideal for fast-moving markets.
Example Use Case
A trader analyzing the S&P 500 may use a gamma factor of 0.92 with a 14-period GWAP to detect shifts in market sentiment during periods of heightened volatility. When the index price crosses above the GWAP, this could signal a potential recovery, prompting a buy entry. Conversely, when the price moves below the GWAP during a correction, it may suggest a short-selling opportunity.
Scientific References
• Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press.
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
• Bandi, F. M., & Russell, J. R. (2006). Microstructure Noise, Realized Variance, and Optimal Sampling. Econometrica.
Triad Trade MatrixOverview
Triad Trade Matrix is an advanced multi-strategy indicator built using Pine Script v5. It is designed to simultaneously track and display key trading metrics for three distinct trading styles on a single chart:
Swing Trading (Swing Supreme):
This mode captures longer-term trends and is designed for trades that typically span several days. It uses customizable depth and deviation parameters to determine swing signals.
Day Trading (Day Blaze):
This mode focuses on intraday price movements. It generates signals that are intended to be executed within a single trading session. The parameters for depth and deviation are tuned to capture more frequent, shorter-term moves.
Scalping (Scalp Surge):
This mode is designed for very short-term trades where quick entries and exits are key. It uses more sensitive parameters to detect rapid price movements suitable for scalping strategies.
Each trading style is represented by its own merged table that displays real-time metrics. The tables update automatically as new trading signals are generated.
Key Features
Multi-Style Tracking:
Swing Supreme (Large): For swing trading; uses a purple theme.
Day Blaze (Medium): For day trading; uses an orange theme.
Scalp Surge (Small): For scalping; uses a green theme.
Real-Time Metrics:
Each table displays key trade metrics including:
Entry Price: The price at which the trade was entered.
Exit Price: The price at which the previous trade was exited.
Position Size: Calculated as the account size divided by the entry price.
Direction: Indicates whether the trade is “Up” (long) or “Down” (short).
Time: The time when the trade was executed (formatted to hours and minutes).
Wins/Losses: The cumulative number of winning and losing trades.
Current Price & PnL: The current price on the chart and the profit/loss computed relative to the entry price.
Duration: The number of bars that the trade has been open.
History Column: A merged summary column that shows the most recent trade’s details (entry, exit, and result).
Customizability:
Column Visibility: Users can toggle individual columns (Ticker, Timeframe, Entry, Exit, etc.) on or off according to their preference.
Appearance Settings: You can customize the table border width, frame color, header background, and text colors.
History Toggle: The merged history column can be enabled or disabled.
Chart Markers: There is an option to show or hide chart markers (labels and lines) that indicate trade entries and exits on the chart.
Trade History Management:
The indicator maintains a rolling history (up to three recent trades per trading style) and displays the latest summary in the merged table.
This history column provides a quick reference to recent performance.
How It Works
Signal Generation & Trade Metrics
Trade Entry/Exit Calculation:
For each trading style, the indicator uses built-in functions (such as ta.lowestbars and ta.highestbars) to analyze price movements. Based on a customizable "depth" and "deviation" parameter, it determines the point of entry for a trade.
Swing Supreme: Uses larger depth/deviation values to capture swing trends.
Day Blaze: Uses intermediate values for intraday moves.
Scalp Surge: Uses tighter parameters to pick up rapid price changes.
Metrics Update:
When a new trade signal is generated (i.e., when the trade entry price is updated), the indicator calculates:
The current PnL as the difference between the current price and the entry price (or vice versa, depending on the trade direction).
The duration as the number of bars since the trade was opened.
The position size using the formula: accountSize / entryPrice.
History Recording:
Each time a new trade is triggered (i.e., when the entry price is updated), a summary string is created (showing entry, exit, and win/loss status) and appended to the corresponding trade history array. The merged table then displays the latest summary from this history.
Table Display
Merged Table Structure:
Each trading style (Swing Supreme, Day Blaze, and Scalp Surge) is represented by a table that has 15 columns. The columns are:
Trade Type (e.g., Swing Supreme)
Ticker
Timeframe
Entry Price
Exit Price
Position Size
Direction
Time of Entry
Account Size
Wins
Losses
Current Price
Current PnL
Duration (in bars)
History (the latest trade summary)
User Customization:
Through the settings panel, users can choose which columns to display.
If a column is toggled off, its cells will remain blank, allowing traders to focus on the metrics that matter most to them.
Appearance & Themes:
The table headers and cell backgrounds are customizable via color inputs. The trading style names are color-coded:
Swing Supreme (Large): Uses a purple theme.
Day Blaze (Medium): Uses an orange theme.
Scalp Surge (Small): Uses a green theme.
How to Use the Indicator
Add the Indicator to Your Chart:
Once published, add "Triad Trade Matrix" to your TradingView chart.
Configure the Settings:
Adjust the Account Size to match your trading capital.
Use the Depth and Deviation inputs for each trading style to fine-tune the signal sensitivity.
Toggle the Chart Markers on if you want visual entry/exit markers on the chart.
Customize which columns are visible via the column visibility toggles.
Enable or disable the History Column to show the merged trade history in the table.
Adjust the appearance settings (colors, border width, etc.) to suit your chart background and preferences.
Interpret the Tables:
Swing Supreme:
This table shows metrics for swing trades.
Look for changes in entry price, PnL, and trade duration to monitor longer-term moves.
Day Blaze:
This table tracks day trading activity.It will update more frequently, reflecting intraday trends.
Scalp Surge:
This table is dedicated to scalping signals.Use it to see quick entry/exit data and rapid profit/loss changes.
The History column (if enabled) gives you a snapshot of the most recent trade (e.g., "E:123.45 X:124.00 Up Win").
Use allerts:
The indicator includes alert condition for new trade entries(both long and short)for each trading style.
Summary:
Triad Trade Matrix provides an robust,multi-dimensional view of your trading performance across swing trading, day trading, and scalping.
Best to be used whith my other indicators
True low high
Vma Ext_Adv_CustomTbl
This indicator is ideal for traders who wish to monitor multiple trading styles simultaneously, with a clear, technical, and real-time display of performance metrics.
Happy Trading!
[COG] Adaptive Squeeze Intensity 📊 Adaptive Squeeze Intensity (ASI) Indicator
🎯 Overview
The Adaptive Squeeze Intensity (ASI) indicator is an advanced technical analysis tool that combines the power of volatility compression analysis with momentum, volume, and trend confirmation to identify high-probability trading opportunities. It quantifies the degree of price compression using a sophisticated scoring system and provides clear entry signals for both long and short positions.
⭐ Key Features
- 📈 Comprehensive squeeze intensity scoring system (0-100)
- 📏 Multiple Keltner Channel compression zones
- 📊 Volume analysis integration
- 🎯 EMA-based trend confirmation
- 🎨 Proximity-based entry validation
- 📱 Visual status monitoring
- 🎨 Customizable color schemes
- ⚡ Clear entry signals with directional indicators
🔧 Components
1. 📐 Squeeze Intensity Score (0-100)
The indicator calculates a total squeeze intensity score based on four components:
- 📊 Band Convergence (0-40 points): Measures the relationship between Bollinger Bands and Keltner Channels
- 📍 Price Position (0-20 points): Evaluates price location relative to the base channels
- 📈 Volume Intensity (0-20 points): Analyzes volume patterns and thresholds
- ⚡ Momentum (0-20 points): Assesses price momentum and direction
2. 🎨 Compression Zones
Visual representation of squeeze intensity levels:
- 🔴 Extreme Squeeze (80-100): Red zone
- 🟠 Strong Squeeze (60-80): Orange zone
- 🟡 Moderate Squeeze (40-60): Yellow zone
- 🟢 Light Squeeze (20-40): Green zone
- ⚪ No Squeeze (0-20): Base zone
3. 🎯 Entry Signals
The indicator generates entry signals based on:
- ✨ Squeeze release confirmation
- ➡️ Momentum direction
- 📊 Candlestick pattern confirmation
- 📈 Optional EMA trend alignment
- 🎯 Customizable EMA proximity validation
⚙️ Settings
🔧 Main Settings
- Base Length: Determines the calculation period for main indicators
- BB Multiplier: Sets the Bollinger Bands deviation multiplier
- Keltner Channel Multipliers: Three separate multipliers for different compression zones
📈 Trend Confirmation
- Four customizable EMA periods (default: 21, 34, 55, 89)
- Optional trend requirement for entry signals
- Adjustable EMA proximity threshold
📊 Volume Analysis
- Customizable volume MA length
- Adjustable volume threshold for signal confirmation
- Option to enable/disable volume analysis
🎨 Visualization
- Customizable bullish/bearish colors
- Optional intensity zones display
- Status monitor with real-time score and state information
- Clear entry arrows and background highlights
💻 Technical Code Breakdown
1. Core Calculations
// Base calculations for EMAs
ema_1 = ta.ema(close, ema_length_1)
ema_2 = ta.ema(close, ema_length_2)
ema_3 = ta.ema(close, ema_length_3)
ema_4 = ta.ema(close, ema_length_4)
// Proximity calculation for entry validation
ema_prox_raw = math.abs(close - ema_1) / ema_1 * 100
is_close_to_ema_long = close > ema_1 and ema_prox_raw <= prox_percent
```
### 2. Squeeze Detection System
```pine
// Bollinger Bands setup
BB_basis = ta.sma(close, length)
BB_dev = ta.stdev(close, length)
BB_upper = BB_basis + BB_mult * BB_dev
BB_lower = BB_basis - BB_mult * BB_dev
// Keltner Channels setup
KC_basis = ta.sma(close, length)
KC_range = ta.sma(ta.tr, length)
KC_upper_high = KC_basis + KC_range * KC_mult_high
KC_lower_high = KC_basis - KC_range * KC_mult_high
```
### 3. Scoring System Implementation
```pine
// Band Convergence Score
band_ratio = BB_width / KC_width
convergence_score = math.max(0, 40 * (1 - band_ratio))
// Price Position Score
price_range = math.abs(close - KC_basis) / (KC_upper_low - KC_lower_low)
position_score = 20 * (1 - price_range)
// Final Score Calculation
squeeze_score = convergence_score + position_score + vol_score + mom_score
```
### 4. Signal Generation
```pine
// Entry Signal Logic
long_signal = squeeze_release and
is_momentum_positive and
(not use_ema_trend or (bullish_trend and is_close_to_ema_long)) and
is_bullish_candle
short_signal = squeeze_release and
is_momentum_negative and
(not use_ema_trend or (bearish_trend and is_close_to_ema_short)) and
is_bearish_candle
```
📈 Trading Signals
🚀 Long Entry Conditions
- Squeeze release detected
- Positive momentum
- Bullish candlestick
- Price above relevant EMAs (if enabled)
- Within EMA proximity threshold (if enabled)
- Sufficient volume confirmation (if enabled)
🔻 Short Entry Conditions
- Squeeze release detected
- Negative momentum
- Bearish candlestick
- Price below relevant EMAs (if enabled)
- Within EMA proximity threshold (if enabled)
- Sufficient volume confirmation (if enabled)
⚠️ Alert Conditions
- 🔔 Extreme squeeze level reached (score crosses above 80)
- 🚀 Long squeeze release signal
- 🔻 Short squeeze release signal
💡 Tips for Usage
1. 📱 Use the status monitor to track real-time squeeze intensity and state
2. 🎨 Pay attention to the color gradient for trend direction and strength
3. ⏰ Consider using multiple timeframes for confirmation
4. ⚙️ Adjust EMA and proximity settings based on your trading style
5. 📊 Use volume analysis for additional confirmation in liquid markets
📝 Notes
- 🔧 The indicator combines multiple technical analysis concepts for robust signal generation
- 📈 Suitable for all tradable markets and timeframes
- ⭐ Best results typically achieved in trending markets with clear volatility cycles
- 🎯 Consider using in conjunction with other technical analysis tools for confirmation
⚠️ Disclaimer
This technical indicator is designed to assist in analysis but should not be considered as financial advice. Always perform your own analysis and risk management when trading.
Twitter Model ICT [TradingFinder] MMXM ERL D + FVG + M15 MSS/SMT🔵 Introduction
The Twitter Model ICT is a trading approach based on ICT (Inner Circle Trader) models, focusing on price movement between external and internal liquidity in lower timeframes. This model integrates key concepts such as Market Structure Shift (MSS), Smart Money Technique (SMT) divergence, and CISD level break to identify precise entry points in the market.
The primary goal of this model is to determine key liquidity levels, such as the previous day’s high and low (PDH/PDL) and align them with the Fair Value Gap (FVG) in the 1-hour timeframe. The overall strategy involves framing trades around the 1H FVG and using the M15 Market Structure Shift (MSS) for entry confirmation.
The Twitter Model ICT is designed to utilize external liquidity levels, such as PDH/PDL, as key entry zones. The model identifies FVG in the 1-hour timeframe, which acts as a magnet for price movement. Additionally, traders confirm entries using M15 Market Structure Shift (MSS) and SMT divergence.
Bullish Twitter Model :
In a bullish setup, the price sweeps the previous day’s low (PDL), and after confirming reversal signals, buys are executed in internal liquidity zones. Conversely, in a bearish setup, the price sweeps the previous day’s high (PDH), and after confirming weakness signals, sells are executed.
Bearish Twitter Model :
In short setups, entries are only executed above the Midnight Open, while in long setups, entries are taken below the Midnight Open. Adhering to these principles allows traders to define precise entry and exit points and analyze price movement with greater accuracy based on liquidity and market structure.
🔵 How to Use
The Twitter Model ICT is a liquidity-based trading strategy that analyzes price movements relative to the previous day’s high and low (PDH/PDL) and Fair Value Gap (FVG). This model is applicable in both bullish and bearish directions and utilizes the 1-hour (1H) and 15-minute (M15) timeframes for entry confirmation.
The price first sweeps an external liquidity level (PDH or PDL) and then provides an entry opportunity based on Market Structure Shift (MSS) and SMT divergence. Additionally, the entry should be positioned relative to the Midnight Open, meaning long entries should occur below the Midnight Open and short entries above it.
🟣 Bullish Twitter Model
In a bullish setup, the price first sweeps the previous day’s low (PDL) and reaches an external liquidity level. Then, in the 1-hour timeframe (1H), a bullish Fair Value Gap (FVG) forms, which serves as the price target.
To confirm the entry, a Market Structure Shift (MSS) in the 15-minute timeframe (M15) should be observed, signaling a trend reversal to the upside. Additionally, SMT divergence with correlated assets can indicate weakness in selling pressure.
Under these conditions, a long position is taken below the Midnight Open, with a stop-loss placed at the lowest point of the recent bearish move. The price target for this trade is the FVG in the 1-hour timeframe.
🟣 Bearish Twitter Model
In a bearish setup, the price first sweeps the previous day’s high (PDH) and reaches an external liquidity level. Then, in the 1-hour timeframe (1H), a bearish Fair Value Gap (FVG) is identified, serving as the trade target.
To confirm entry, a Market Structure Shift (MSS) in the 15-minute timeframe (M15) should form, signaling a trend shift to the downside. If an SMT divergence is present, it can provide additional confirmation for the trade.
Once these conditions are met, a short position is taken above the Midnight Open, with a stop-loss placed at the highest level of the recent bullish move. The trade's price target is the FVG in the 1-hour timeframe.
🔵 Settings
Bar Back Check : Determining the return of candles to identify the CISD level.
CISD Level Validity : CISD level validity period based on the number of candles.
Daily Position : Determines whether only the first signal of the day is considered or if signals are evaluated throughout the entire day.
Session : Specifies in which trading sessions the indicator will be active.
Second Symbol : This setting allows you to select another asset for comparison with the primary asset. By default, "XAUUSD" (Gold) is set as the second symbol, but you can change it to any currency pair, stock, or cryptocurrency. For example, you can choose currency pairs like EUR/USD or GBP/USD to identify divergences between these two assets.
Divergence Fractal Periods : This parameter defines the number of past candles to consider when identifying divergences. The default value is 2, but you can change it to suit your preferences. This setting allows you to detect divergences more accurately by selecting a greater number of candles.
The indicator allows displaying sessions based on various time zones. The user can select one of the following options :
UTC (Coordinated Universal Time)
Local Time of the Session
User’s Local Time
Show Open Price : Displays the New York market opening price.
Show PDH / PDL : Displays the previous day’s high and low to identify potential entry points.
Show SMT Divergence : Displays lines and labels for bullish ("+SMT") and bearish ("-SMT") divergences.
🔵 Conclusion
The Twitter Model ICT is an effective approach for analyzing and executing trades in financial markets, utilizing a combination of liquidity principles, market structure, and SMT confirmations to identify optimal entry and exit points.
By analyzing the previous day’s high and low (PDH/PDL), Fair Value Gaps (FVG), and Market Structure Shift (MSS) in the 1H and M15 timeframes, traders can pinpoint liquidity-driven trade opportunities. Additionally, considering the Midnight Open level helps traders avoid random entries and ensures better trade placement.
By applying this model, traders can interpret market movements based on liquidity flow and structural changes, allowing them to fine-tune their trading decisions with higher precision. Ultimately, the Twitter Model ICT provides a structured and logical approach for traders who seek to trade based on liquidity behavior and trend shifts in the market.
Johnny's Volatility-Driven Trend Identifier w/ Reversal SignalsJohnny's Volatility-Driven Trend Identifier w/ Reversal Signals is designed to identify high-probability trend shifts and reversals by incorporating volatility, momentum, and impulse-based filtering. It is specifically built for traders who want to capture strong trend movements while minimizing false signals caused by low volatility noise.
By leveraging Rate of Change (ROC), Relative Strength Index (RSI), and Average True Range (ATR)-based volatility detection, the indicator dynamically adapts to market conditions. It highlights breakout trends, reversals, and early signs of momentum shifts using strategically placed labels and color-coded trend visualization.
Inspiration taken from Top G indicator .
What This Indicator Does
The Volatility-Driven Trend Identifier works by:
Measuring Market Extremes & Momentum:
Uses ROC normalization with standard deviation to identify impulse moves in price action.
Implements RSI filtering to determine overbought/oversold conditions that validate trend strength.
Utilizes ATR-based volatility tracking to ensure signals only appear when meaningful market movements are occurring.
Identifying Key Trend Events:
Power Peak (🔥): Marks a confirmed strong downtrend, ideal for shorting opportunities.
Surge (🚀): Indicates a confirmed strong uptrend, signaling a potential long entry.
Soft Surge (↗): Highlights a mild bullish reentry or early uptrend formation.
Soft Peak (↘): Shows a mild bearish reentry or early downtrend formation.
Providing Adaptive Filtering for Reliable Signals:
Filters out weak trends with a volatility check, ensuring signals appear only in strong market conditions.
Implements multi-level confirmation by combining trend strength metrics, preventing false breakouts.
Uses gradient-based visualization to color-code market sentiment for quick interpretation.
What This Indicator Signals
Breakouts & Impulse Moves: 🚀🔥
The Surge (🚀) and Power Peak (🔥) labels indicate confirmed momentum breakouts, where the trend has been validated by a combination of ROC impulse, RSI confirmation, and ATR volatility filtering.
These signals suggest that the market is entering a strong trend, and traders can align their entries accordingly.
Early Trend Formation & Reentries: ↗ ↘
The Soft Surge (↗) and Soft Peak (↘) labels indicate areas where a trend might be forming, but is not yet fully confirmed.
These signals help traders anticipate potential entries before the trend gains full strength.
Volatility-Adaptive Trend Filtering: 📊
Since the indicator only activates in volatile conditions, it avoids the pitfalls of low-range choppy markets where false signals frequently occur.
ATR-driven adaptive windowing allows the indicator to dynamically adjust its sensitivity based on real-time volatility conditions.
How to Use This Indicator
1. Identifying High-Probability Entries
Bullish Entries (Long Trades)
Look for 🚀 Surge signals in an uptrend.
Confirm with RSI (should be above 50 for momentum).
Ensure volatility is increasing to validate the breakout.
Use ↗ Soft Surge signals for early entries before the trend fully confirms.
Bearish Entries (Short Trades)
Look for 🔥 Power Peak signals in a downtrend.
RSI should be below 50, indicating downward momentum.
Volatility should be rising, ensuring market momentum is strong.
Use ↘ Soft Peak signals for early entries before a full bearish confirmation.
2. Avoiding False Signals
Ignore signals when the market is ranging (low ATR).
Check RSI and ROC alignment to ensure trend confirmation.
Use additional confluences (e.g., price action, support/resistance levels, moving averages) for enhanced accuracy.
3. Trend Confirmation & Filtering
The stronger the trend, the higher the likelihood that Surge (🚀) and Power Peak (🔥) signals will continue in their direction.
Soft Surge (↗) and Soft Peak (↘) act as early warning signals before major breakouts occur.
What Makes This a Machine Learning-Inspired Moving Average?
While this indicator is not a direct implementation of machine learning (as Pine Script lacks AI/ML capabilities), it mimics machine learning principles by adapting dynamically to market conditions using the following techniques:
Adaptive Trend Selection:
It does not rely on fixed moving averages but instead adapts dynamically based on volatility expansion and momentum detection.
ATR-based filtering adjusts the indicator’s sensitivity to real-time conditions.
Multi-Factor Confirmation (Feature Engineering Equivalent in ML):
Combines ROC, RSI, and ATR in a structured way, similar to how ML models use multiple inputs to filter and classify data.
Implements conditional trend recognition, ensuring that only valid signals pass through the filter.
Noise Reduction with Data Smoothing:
The algorithm avoids false signals by incorporating trend intensity thresholds, much like how ML models remove outliers to refine predictions.
Adaptive filtering ensures that low-volatility environments do not produce misleading signals.
Why Use This Indicator?
✔ Reduces False Signals: Multi-factor validation ensures only high-confidence signals are triggered.
✔ Works in All Market Conditions: Volatility-adaptive nature allows the indicator to perform well in both trending and ranging markets.
✔ Great for Swing & Intraday Trading: It helps spot momentum shifts early and allows traders to catch major market moves before they fully develop.
✔ Visually Intuitive: Color-coded trends and clear signal markers make it easy to interpret.
9-20 EMA Crossover with TP and SL9-20 EMA Crossover: This script tracks the crossover of the 9-period EMA and the 20-period EMA.
When the 9 EMA crosses above the 20 EMA, a buy signal is triggered.
When the 9 EMA crosses below the 20 EMA, a sell signal is triggered.
Take Profit and Stop Loss Levels:
The take profit for a long position is set at 3% above the entry price (close * 1.03).
The stop loss for a long position is set at 1% below the entry price (close * 0.99).
The take profit for a short position is set at 3% below the entry price (close * 0.97).
The stop loss for a short position is set at 1% above the entry price (close * 1.01).
Leverage: The strategy uses 20x leverage for both long and short positions (leverage=20).
Alerts: Alerts are set up for the buy signal when the 9 EMA crosses above the 20 EMA and the sell signal when the 9 EMA crosses below the 20 EMA. These alerts can be used with a webhook to trigger trades on Binance Futures.
Strategy:
For long trades: The strategy enters a long position and sets a take profit at 3% above the entry price and a stop loss at 1% below the entry price.
For short trades: The strategy enters a short position and sets a take profit at 3% below the entry price and a stop loss at 1% above the entry price.
Martingale8MARTINGALE8 Indicator: Comprehensive User Guide
Welcome to the MARTINGALE8 Indicator, your ultimate tool for implementing a customizable martingale trading strategy directly on TradingView! Whether you're a beginner trader or an experienced strategist, this indicator offers flexibility and clarity, empowering you to trade with confidence. Let’s dive into how you can make the most of it!
What Is the Martingale Principle?
The martingale strategy is a betting technique often used in gambling and trading. The idea is simple: double down on losing positions so that when a trade eventually succeeds, the profits will recover all previous losses and yield a small profit. In trading, this translates to placing incrementally larger buy orders as the price moves against your initial position, assuming the price will eventually reverse in your favor.
The martingale principle works under the asumption of mean reversion —that the price will eventually recover to a point where all accumulated losses are recouped, and a profit is made. By increasing order sizes at lower levels, the average entry price moves closer to the current price, reducing the price move required to reach profitability. However, like any strategy, it carries risks — if the price continues to move against your position without reversing, losses can escalate quickly .
What Does MARTINGALE8 Do?
The MARTINGALE8 Indicator is an open source script designed to:
Calculate multiple price levels (buy and take-profit) using a martingale strategy.
Allow full customization of entry size, order deviation, profit targets, and order multipliers.
Visualize key trading levels directly on the chart for better decision-making.
Provide helpful labels with real-time metrics like total cost, range analysis, and high-volume bar prices.
This indicator is ideal for traders looking to automate and refine their martingale-based trading approaches.
Features
1. Customizable Inputs
You have complete control over key parameters:
Start Price: Set a custom starting price, or let it default to the market price.
Entry Size: Choose your initial trade size (default: equivalent to 7.5 USDT).
Order Multiplier: Adjust the size of each subsequent order in the martingale sequence.
Order Deviation: Define the percentage deviation for each buy level.
Profit Deviation: Determine the target percentage deviation for take-profit levels.
Length: Specify the lookback period for market analysis (default: 84 bars).
2. Market Analysis
The script calculates key metrics, including:
Highest Volume Bar (HVB): Identifies the bar with the highest trading volume in the selected period.
Range Analysis: Computes the high-to-low range percentage to help you understand market volatility.
3. Martingale Levels
Automatically generates :
10 Buy Levels: Strategically placed below the starting price.
Take-Profit Level: A target above the starting price based on the profit deviation.
4. Cost Calculation
The script calculates the total cost of all orders, including a 10% buffer for safety, so you can plan your capital allocation effectively.
5. Visual Elements
The indicator draws clean and intuitive lines for:
Take-Profit Level: Highlighted in fuchsia.
Buy Levels: Clearly marked with aqua lines.
Zero Line: Your base price, shown in white.
Additional labels provide:
A summary of key metrics like total cost, entry price, and range.
Precise price values for the take-profit and lowest buy levels.
How to Use MARTINGALE8
Step 1: Add the Indicator to Your Chart
Click on the “Indicators” tab in TradingView.
Search for “MARTINGALE8” and add it to your chart.
Step 2: Configure the Inputs
Navigate to the Settings menu of the indicator and adjust the following parameters:
Start Price : Set your starting price or leave it as 0 to use the current market price.
Entry Size : Define the size of your initial trade (e.g., 7.5 USDT).
Order Multiplier : Choose how much larger each subsequent order should be.
Order Deviation : Specify the percentage distance between buy levels.
Profit Deviation : Set your desired percentage for the take-profit level.
Length : Adjust the number of bars to analyze for high volume.
Step 3: Visualize the Levels
The indicator will plot:
A white line for the base price.
Aqua lines for the buy levels.
A fuchsia line for the take-profit level.
Step 4: Monitor the Labels
Look for the summary label on the chart, which shows:
Total cost of the martingale orders.
Entry price and key market metrics (range, high-volume bar price).
Tips for Optimal Use
Adjust Inputs to Match Market Conditions : Experiment with order and profit deviations to account for volatile or steady markets.
Manage Risk : Use the cost calculation feature to ensure you allocate capital responsibly.
Technical Details
The script is written in Pine Script v6 and uses:
Switch Statements : For flexible default values.
Line Objects : To draw and update key price levels dynamically.
Labels : To display relevant trading metrics.
I’m glad to share this tool with the TradingView community. If you enjoy using MARTINGALE8, please keep it going and share your feedback. Let’s trade smarter, not harder!
Buy/Sell Break and RetestThis script is a Pine Script indicator for TradingView titled **"Buy/Sell Break and Retest"**. Here's a description of its functionality:
### Purpose:
The script identifies potential **buy** and **sell entry levels** based on break-and-retest patterns in the market. It works by analyzing higher timeframe data (e.g., 1-hour) and marking entries on a lower timeframe (e.g., 1-minute).
### Key Features:
1. **Configurable Timeframes**:
- `Analysis Timeframe`: Used for identifying break-and-retest signals (default: 1-hour).
- `Entry Timeframe`: Used for marking and plotting entries (default: 1-minute).
2. **Buy and Sell Signals**:
- A **sell entry** is triggered when a bearish candle (close < open) is identified in the analysis timeframe.
- A **buy entry** is triggered when a bullish candle (close > open) is identified in the analysis timeframe.
3. **Retest Logic**:
- For sell signals: The retest is validated when the price breaks below the identified sell level.
- For buy signals: The retest is validated when the price breaks above the identified buy level.
4. **Visual Indicators**:
- Entry levels are marked with labels:
- **Buy Entry**: Green labels are placed at bullish candle opens.
- **Sell Entry**: Red labels are placed at bearish candle closes.
- Plots the levels for easy reference:
- **Sell Level**: Displayed as red circles on the chart.
- **Buy Level**: Displayed as green circles on the chart.
5. **Dynamic Updates**:
- Levels are cleared when invalidated by the price action.
### Use Case:
This indicator helps traders spot break-and-retest opportunities by:
- Allowing higher timeframe analysis to determine trend direction and key levels.
- Providing actionable buy and sell entry points on lower timeframes for precision.
Let me know if you'd like further clarification or improvements!
Precision Trade Zone By KittisakThis indicator is designed for Money Management calculations, helping to facilitate risk management in trading, determining suitable leverage based on acceptable risk, and adjusting the Stop Loss level to align with the calculated leverage.
Abbreviation Descriptions
LR : Suitable Leverage.
EP : Entry Price.
BEP : Break-Even Point (a point where you can move your Stop Loss to prevent losses once the price reaches a certain level).
SL : Stop Loss (a recalculated Stop Loss level to match the leverage. You should use this as the Stop Loss price instead of the initial level you set).
TP : Take Profit (a point where you take profit based on the defined risk-reward ratio).
Note
When first activating the indicator, an error may occur, and no output will be displayed. This happens because you must first specify the Entry Price and Stop Loss in the indicator settings.
How Much Leverage Should You Use?
It may seem like a simple question but is difficult to answer.
Method for Calculating Suitable Leverage
Use the formula:
Leverage = Acceptable Loss / (Distance between Entry Price and Stop Loss + (Buy Fee + Sell Fee))
Calculating the Correct Stop Loss Point
(Stop Loss levels will be slightly adjusted or extended)
For Long Positions :
New Stop Loss = Entry Price * (1 - Acceptable Loss / (Calculated Leverage * 100))
For Short Positions :
New Stop Loss = Entry Price * (1 + Acceptable Loss / (Calculated Leverage * 100))
Calculating the Correct Take Profit Point
(Take Profit levels will be slightly adjusted or extended)
For Long Positions :
Take Profit = Entry Price * (1 + (Acceptable Loss / (Calculated Leverage * 100) * RR) + ((Buy Fee + Sell Fee) / 100))
For Short Positions :
Take Profit = Entry Price * (1 - (Acceptable Loss / (Calculated Leverage * 100) * RR) + ((Buy Fee + Sell Fee) / 100))
Benefits of This Calculation
1. Accurate Risk Assessment
The calculated leverage accounts for trading fees. For example, if you aim for a 2% loss, this method ensures the actual loss is exactly 2%, not more (e.g., 2% plus fees).
2. Eliminates Guesswork
Randomly setting leverage can lead to risks because the Stop Loss level may not align with your position. This calculation ensures that the leverage aligns precisely with your desired Stop Loss level.
3. Realistic Profit Targets
For example, with a 2% acceptable loss and a 1:2 RR, you expect a 4% profit. However, without this calculation, fees may reduce your profit below 4%. This method includes fees, ensuring your profit matches the intended target.
Caution
This indicator does not account for slippage or requotes. Use it with caution and allow a buffer for slippage in your calculations.
Indicator นี้มีไว้สำหรับคำนวณ Money Management ซึ่งจะช่วยอำนวยความสะดวกในการจัดการความเสี่ยงในการเทรด การคำนวณ Leverage ที่เหมาะสมกับความเสี่ยงที่คุณยอมรับได้ และจัดการจุด Stop Loss ให้เหมาะสมกับ Leverage นั้น
คำอธิบายเกี่ยวกับคำย่อ
LR หมายถึง Leverage ที่เหมาะสม
EP หมายถึง Entry Price หรือราคาเข้าซื้อ
BEP หมายถึง Break-Even Point หรือจุดคุ้มทุน (คุณสามารถย้าย Stop Loss มาที่จุดนี้เมื่อราคาไปถึงจุดหนึ่งเพื่อป้องกันการขาดทุนได้)
SL หมายถึง Stop Loss (ซึ่งเป็น Stop Loss ที่คำนวณใหม่เพื่อให้ตำแหน่งเหมาะสมกับ Leverage ที่คำนวณได้ คุณควรใช้จุดนี้เพื่อเป็นราคา Stop Loss แทนจุด Stop Loss ที่คุณกำหนดไว้ในตอนแรก)
TP หมายถึง Take Profit (เป็นจุดที่คุณจะขายทำกำไรตาม RR ที่กำหนดไว้)
* หมายเหตุ เมื่อเริ่มเปิด Indicator จะเกิด Error ขึ้น และไม่มีผลลัพท์ใด ๆ แสดงให้เห็น นั่นเป็นเพราะคุณต้องเข้าไปกำหนด Entry Price และ Stop Loss ในการตั้งค่าของ Indicator เสียก่อน
ต้องใช้ Leverage เท่าไหร่? มันเป็นคำถามที่ดูเหมือนง่าย แต่ตอบยาก
วิธีคำนวณ Leverage ที่เหมาะสม ใช้สมการคือ
Levarage = การขาดทุนที่ยอมรับได้ / (ระยะห่างระหว่าง Entry Price และ Stop Loss + (ค่าธรรมเนียมซื้อ + ค่าธรรมเนียมขาย))
นำผลลัพท์ Leverage ที่ได้มาคำนวณเพื่อหาจุด Stop Loss ที่ถูกต้อง (จุดของ Stop Loss จะมีการยืดขยายออกไปเล็กน้อย) โดยใช้สมการ
ตำแหน่ง Stop Loss ใหม่ = Entry Price * (1 - การขาดทุนที่ยอมรับได้ / (Leverage ที่คำนวณได้ * 100)) // สำหรับ Long
ตำแหน่ง Stop Loss ใหม่ = Entry Price * (1 + การขาดทุนที่ยอมรับได้ / (Leverage ที่คำนวณได้ * 100)) // สำหรับ Short
นำผลลัพท์ Leverage ที่ได้มาคำนวณเพื่อหาจุด Take Profit ที่ถูกต้อง (จุดของ Take Profit จะมีการยืดขยายออกไปเล็กน้อย) โดยใช้สมการ
ตำแหน่ง Take Profit = Entry Price * (1 + (การขาดทุนที่ยอมรับได้ / (Leverage ที่คำนวณได้ * 100) * RR) + ((ค่าธรรมเนียมซื้อ + ค่าธรรมเนียมขาย) / 100)) // สำหรับ Long
ตำแหน่ง Take Profit = Entry Price * (1 - (การขาดทุนที่ยอมรับได้ / (Leverage ที่คำนวณได้ * 100) * RR) + ((ค่าธรรมเนียมซื้อ + ค่าธรรมเนียมขาย) / 100)) // สำหรับ Short
ข้อดีของการคำนวณคือ
1. คุณจะได้ค่า Leverage ที่เหมาะสมกับความเสี่ยงที่คุณยอมรับได้โดยรวมค่าธรรมเนียมเข้าไปในนั้นแล้ว นั่นหมายความว่า ความสูญเสียจะเป็น 2% (ตามตัวอย่าง) จริง ๆ ไม่ใช่ 2% และถูกหักค่าธรรมเนียมเพิ่มอีก กลายเป็นสูญเสียมากกว่า 2%
2. การตั้ง Leverage มั่ว ๆ กลายเป็นความเสี่ยง นั่นเพราะตำแหน่งของ Stop Loss ไม่ได้อยู่ในจุดที่ควรจะเป็น การคำนวณนี้ช่วยให้คุณได้ Leverage ในตำแหน่ง Stop Loss ที่คุณต้องการโดยแท้จริง
3. ผลกำไรที่ได้รับตรงกับความต้องการจริง ๆ เช่น การขาดทุนที่ยอมรับได้ 2% และ RR 1:2 สิ่งที่คุณคิดคือกำไร 4% แต่จริง ๆ แล้วไม่ถึง 4% นั่นเพราะว่าโดนหักค่าธรรมเนียมไปส่วนหนึ่ง การคำนวณนี้ได้รวมค่าธรรมเนียมให้แล้ว คุณจึงได้กำไรที่ 4% อย่างถูกต้องตามต้องการ
ข้อควรระวัง
Indicator นี้ไม่ได้มีการควบคุมความเสี่ยงในเรื่องของ slippage หรือ requote โปรดใช้งานอย่างระมัดระวังและมีการเผื่อระยะสำหรับ slippage ด้วย
Mean Reversion Pro Strategy [tradeviZion]Mean Reversion Pro Strategy : User Guide
A mean reversion trading strategy for daily timeframe trading.
Introduction
Mean Reversion Pro Strategy is a technical trading system that operates on the daily timeframe. The strategy uses a dual Simple Moving Average (SMA) system combined with price range analysis to identify potential trading opportunities. It can be used on major indices and other markets with sufficient liquidity.
The strategy includes:
Trading System
Fast SMA for entry/exit points (5, 10, 15, 20 periods)
Slow SMA for trend reference (100, 200 periods)
Price range analysis (20% threshold)
Position management rules
Visual Elements
Gradient color indicators
Three themes (Dark/Light/Custom)
ATR-based visuals
Signal zones
Status Table
Current position information
Basic performance metrics
Strategy parameters
Optional messages
📊 Strategy Settings
Main Settings
Trading Mode
Options: Long Only, Short Only, Both
Default: Long Only
Position Size: 10% of equity
Starting Capital: $20,000
Moving Averages
Fast SMA: 5, 10, 15, or 20 periods
Slow SMA: 100 or 200 periods
Default: Fast=5, Slow=100
🎯 Entry and Exit Rules
Long Entry Conditions
All conditions must be met:
Price below Fast SMA
Price below 20% of current bar's range
Price above Slow SMA
No existing position
Short Entry Conditions
All conditions must be met:
Price above Fast SMA
Price above 80% of current bar's range
Price below Slow SMA
No existing position
Exit Rules
Long Positions
Exit when price crosses above Fast SMA
No fixed take-profit levels
No stop-loss (mean reversion approach)
Short Positions
Exit when price crosses below Fast SMA
No fixed take-profit levels
No stop-loss (mean reversion approach)
💼 Risk Management
Position Sizing
Default: 10% of equity per trade
Initial capital: $20,000
Commission: 0.01%
Slippage: 2 points
Maximum one position at a time
Risk Control
Use daily timeframe only
Avoid trading during major news events
Consider market conditions
Monitor overall exposure
📊 Performance Dashboard
The strategy includes a comprehensive status table displaying:
Strategy Parameters
Current SMA settings
Trading direction
Fast/Slow SMA ratio
Current Status
Active position (Flat/Long/Short)
Current price with color coding
Position status indicators
Performance Metrics
Net Profit (USD and %)
Win Rate with color grading
Profit Factor with thresholds
Maximum Drawdown percentage
Average Trade value
📱 Alert Settings
Entry Alerts
Long Entry (Buy Signal)
Short Entry (Sell Signal)
Exit Alerts
Long Exit (Take Profit)
Short Exit (Take Profit)
Alert Message Format
Strategy name
Signal type and direction
Current price
Fast SMA value
Slow SMA value
💡 Usage Tips
Consider starting with Long Only mode
Begin with default settings
Keep track of your trades
Review results regularly
Adjust settings as needed
Follow your trading plan
⚠️ Disclaimer
This strategy is for educational and informational purposes only. It is not financial advice. Always:
Conduct your own research
Test thoroughly before live trading
Use proper risk management
Consider your trading goals
Monitor market conditions
Never risk more than you can afford to lose
📋 Release Notes
14 January 2025
Added New Fast & Slow SMA Options:
Fibonacci-based periods: 8, 13, 21, 144, 233, 377
Additional period: 50
Complete Fast SMA options now: 5, 8, 10, 13, 15, 20, 21, 34, 50
Complete Slow SMA options now: 100, 144, 200, 233, 377
Bug Fixes:
Fixed Maximum Drawdown calculation in the performance table
Now using strategy.max_drawdown_percent for accurate DD reporting
Previous version showed incorrect DD values
Performance metrics now accurately reflect trading results
Performance Note:
Strategy tested with Fast/Slow SMA 13/377
Test conducted with 10% equity risk allocation
Daily Timeframe
For Beginners - How to Modify SMA Levels:
Find this line in the code:
fastLength = input.int(title="Fast SMA Length", defval=5, options= )
To add a new Fast SMA period: Add the number to the options list, e.g.,
To remove a Fast SMA period: Remove the number from the options list
For Slow SMA, find:
slowLength = input.int(title="Slow SMA Length", defval=100, options= )
Modify the options list the same way
⚠️ Note: Keep the periods that make sense for your trading timeframe
💡 Tip: Test any new combinations thoroughly before live trading
"Trade with Discipline, Manage Risk, Stay Consistent" - tradeviZion
IU Trailing Stop Loss MethodsThe 'IU Trailing Stop Loss Methods' it's a risk management tool which allows users to apply 12 trailing stop-loss (SL) methods for risk management of their trades and gives live alerts when the trailing Stop loss has hit. Below is a detailed explanation of each input and the working of the Script.
Main Inputs:
- bar_time: Specifies the date from which the trade begins and entry price will be the open of the first candle.
- entry_type: Choose between 'Long' or 'Short' positions.
- trailing_method: Select the trailing stop-loss method. Options include ATR, Parabolic SAR, Supertrend, Point/Pip based, Percentage, EMA, Highest/Lowest, Standard Deviation, and multiple target-based methods.
- exit_after_close: If checked, exits the trade only after the candle closes.
Optional Inputs:
ATR Settings:
- atr_Length: Length for the ATR calculation.
- atr_factor: ATR multiplier for SL calculation.
Parabolic SAR Settings:
- start, increment, maximum: Parameters for the Parabolic SAR indicator.
Supertrend Settings:
- supertrend_Length, supertrend_factor: Length and factor for the Supertrend indicator.
Point/Pip Based:
- point_base: Set trailing SL in points/pips.
Percentage Based:
- percentage_base: Set SL as a percentage of entry price.
EMA Settings:
- ema_Length: Length for EMA calculation.
Standard Deviation Settings:
- std_Length, std_factor: Length and factor for standard deviation calculation.
Highest/Lowest Settings:
- highest_lowest_Length: Length for the highest/lowest SL calculation.
Target-Based Inputs:
- ATR, Point, Percentage, and Standard Deviation based target SL settings with customizable lengths and multipliers.
Entry Logic:
- Trades initiate based on the entry_type selected and the specified bar_time.
- If Long is selected, a long trade is initiated when the conditions match, and vice versa for Short.
Trailing Stop-Loss (SL) Methods Explained:
The strategy dynamically adjusts stop-loss based on the chosen method. Each method has its calculation logic:
- ATR: Stop-loss calculated using ATR multiplied by a user-defined factor.
- Parabolic SAR: Uses the Parabolic SAR indicator for trailing stop-loss.
- Supertrend: Utilizes the Supertrend indicator as the stop-loss line.
- Point/Pip Based: Fixed point-based stop-loss.
- Percentage Based: SL set as a percentage of entry price.
- EMA: SL based on the Exponential Moving Average.
- Highest/Lowest: Uses the highest high or lowest low over a specified period.
- Standard Deviation: SL calculated using standard deviation.
Exit Conditions:
- If exit_after_close is enabled, the position will only close after the candle confirms the stop-loss hit.
- If exit_after_close is disabled, the strategy will close the trade immediately when the SL is breached.
Visualization:
The script plots the chosen trailing stop-loss method on the chart for easy visualization.
Target-Based Trailing SL Logic:
- When a position is opened, the strategy calculates the initial stop-loss and progressively adjusts it as the price moves favorably.
- Each SL adjustment is stored in an array for accurate tracking and visualization.
Alerts and Labels:
- When the Entry or trailing stop loss is hit this scripts draws a label and give alert to the user that trailing stop has been hit for the trade.
Note - on the historical data The Script will show nothing if the entry and the exit has happened on the same candle, because we don't know what was hit first SL or TP (basically how the candle was formed on the lower timeframe).
Summary:
This script offers flexible trailing stop-loss options for traders who want dynamic risk management in their strategies. By offering multiple methods like ATR, SAR, Supertrend, and EMA, it caters to various trading styles and risk preferences.
00 Averaging Down Backtest Strategy by RPAlawyer v21FOR EDUCATIONAL PURPOSES ONLY! THE CODE IS NOT YET FULLY DEVELOPED, BUT IT CAN PROVIDE INTERESTING DATA AND INSIGHTS IN ITS CURRENT STATE.
This strategy is an 'averaging down' backtester strategy. The goal of averaging/doubling down is to buy more of an asset at a lower price to reduce your average entry price.
This backtester code proves why you shouldn't do averaging down, but the code can be developed (and will be developed) further, and there might be settings even in its current form that prove that averaging down can be done effectively.
Different averaging down strategies exist:
- Linear/Fixed Amount: buy $1000 every time price drops 5%
- Grid Trading: Placing orders at price levels, often with increasing size, like $1000 at -5%, $2000 at -10%
- Martingale: doubling the position size with each new entry
- Reverse Martingale: decreasing position size as price falls: $4000, then $2000, then $1000
- Percentage-Based: position size based on % of remaining capital, like 10% of available funds at each level
- Dynamic/Adaptive: larger entries during high volatility, smaller during low
- Logarithmic: position sizes increase logarithmically as price drops
Unlike the above average costing strategies, it applies averaging down (I use DCA as a synonym) at a very strong trend reversal. So not at a certain predetermined percentage negative PNL % but at a trend reversal signaled by an indicator - hence it most closely resembles a dynamically moving grid DCA strategy.
Both entering the trade and averaging down assume a strong trend. The signals for trend detection are provided by an indicator that I published under the name '00 Parabolic SAR Trend Following Signals by RPAlawyer', but any indicator that generates numeric signals of 1 and -1 for buy and sell signals can be used.
The indicator must be connected to the strategy: in the strategy settings under 'External Source' you need to select '00 Parabolic SAR Trend Following Signals by RPAlawyer: Connector'. From this point, the strategy detects when the indicator generates buy and sell signals.
The strategy considers a strong trend when a buy signal appears above a very conservative ATR band, or a sell signal below the ATR band. The conservative ATR is chosen to filter ranging markets. This very conservative ATR setting has a default multiplier of 8 and length of 40. The multiplier can be increased up to 10, but there will be very few buy and sell signals at that level and DCA requirements will be very high. Trade entry and DCA occur at these strong trends. In the settings, the 'ATR Filter' setting determines the entry condition (e.g., ATR Filter multiplier of 9), and the 'DCA ATR' determines when DCA will happen (e.g., DCA ATR multiplier of 6).
The DCA levels and DCA amounts are determined as follows:
The first DCA occurs below the DCA Base Deviation% level (see settings, default 3%) which acts as a threshold. The thick green line indicates the long position avg price, and the thin red line below the green line indicates the 3% DCA threshold for long positions. The thick red line indicates the short position avg price, and the thin red line above the thick red line indicates the short position 3% DCA threshold. DCA size multiplier defines the DCA amount invested.
If the loss exceeds 3% AND a buy signal arrives below the lower ATR band for longs, or a sell signal arrives above the upper ATR band for shorts, then the first DCA will be executed. So the first DCA won't happen at 3%, rather 3% is a threshold where the additional condition is that the price must close above or below the ATR band (let's say the first DCA occured at 8%) – this is why the code resembles a dynamic grid strategy, where the grid moves such that alongside the first 3% threshold, a strong trend must also appear for DCA. At this point, the thick green/red line moves because the avg price is modified as a result of the DCA, and the thin red line indicating the next DCA level also moves. The next DCA level is determined by the first DCA level, meaning modified avg price plus an additional +8% + (3% * the Step Scale Multiplier in the settings). This next DCA level will be indicated by the modified thin red line, and the price must break through this level and again break through the ATR band for the second DCA to occur.
Since all this wasn't complicated enough, and I was always obsessed by the idea that when we're sitting in an underwater position for days, doing DCA and waiting for the price to correct, we can actually enter a short position on the other side, on which we can realize profit (if the broker allows taking hedge positions, Binance allows this in Europe).
This opposite position in this strategy can open from the point AFTER THE FIRST DCA OF THE BASE POSITION OCCURS. This base position first DCA actually indicates that the price has already moved against us significantly so time to earn some money on the other side. Breaking through the ATR band is also a condition for entry here, so the hedge position entry is not automatic, and the condition for further DCA is breaking through the DCA Base Deviation (default 3%) and breaking through the ATR band. So for the 'hedge' or rather opposite position, the entry and further DCA conditions are the same as for the base position. The hedge position avg price is indicated by a thick black line and the Next Hedge DCA Level is indicated by a thin black line.
The TPs are indicated by green labels for base positions and red labels for hedge positions.
No SL built into the strategy at this point but you are free to do your coding.
Summary data can be found in the upper right corner.
The fantastic trend reversal indicator Machine learning: Lorentzian Classification by jdehorty can be used as an external indicator, choose 'backtest stream' for the external source. The ATR Band multiplicators need to be reduced to 5-6 when using Lorentz.
The code can be further developed in several aspects, and as I write this, I already have a few ideas 😊
