TJR asia session sweepThe TJR Asia Session Sweep is a liquidity-based trading strategy that focuses on the Asian session high and low range. During the London open, price often sweeps (breaks) one side of that range to grab liquidity — triggering stop hunts. After the sweep, traders look for a break of structure (BOS) and enter in the opposite direction of the sweep.
"range" için komut dosyalarını ara
BOCS Channel Scalper Strategy - Automated Mean Reversion System# BOCS Channel Scalper Strategy - Automated Mean Reversion System
## WHAT THIS STRATEGY DOES:
This is an automated mean reversion trading strategy that identifies consolidation channels through volatility analysis and executes scalp trades when price enters entry zones near channel boundaries. Unlike breakout strategies, this system assumes price will revert to the channel mean, taking profits as price bounces back from extremes. Position sizing is fully customizable with three methods: fixed contracts, percentage of equity, or fixed dollar amount. Stop losses are placed just outside channel boundaries with take profits calculated either as fixed points or as a percentage of channel range.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This strategy is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Version**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the scalper ideal for active day traders who want continuous opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased trade frequency also means higher commission costs and requires tighter risk management.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The strategy normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The strategy tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The strategy uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. This captures mean reversion opportunities as price reaches channel extremes.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents signal spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long signal will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The strategy includes a multi-timeframe ATR filter to avoid trading during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while trading on 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Trading enabled
- If ATR < threshold: No signals fire
This prevents entries during dead zones where mean reversion is unreliable due to insufficient price movement.
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. Larger percentages aim for opposite channel edge.
### Stop Loss Placement:
Stop losses are placed just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. If price breaks through, the range is no longer valid and position exits.
### Trade Execution Logic:
When entry conditions are met (price in zone, cooldown satisfied, ATR filter passed, no existing position):
1. Calculate entry price at zone boundary
2. Calculate TP and SL based on selected method
3. Execute strategy.entry() with calculated position size
4. Place strategy.exit() with TP limit and SL stop orders
5. Update info table with active trade details
The strategy enforces one position at a time by checking strategy.position_size == 0 before entry.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
### Position Sizing System:
Three methods calculate position size:
**Fixed Contracts**:
- Uses exact contract quantity specified in settings
- Best for futures traders (e.g., "trade 2 NQ contracts")
**Percentage of Equity**:
- position_size = (strategy.equity × equity_pct / 100) / close
- Dynamically scales with account growth
**Cash Amount**:
- position_size = cash_amount / close
- Maintains consistent dollar exposure regardless of price
## INPUT PARAMETERS:
### Position Sizing:
- **Position Size Type**: Choose Fixed Contracts, % of Equity, or Cash Amount
- **Number of Contracts**: Fixed quantity per trade (1-1000)
- **% of Equity**: Percentage of account to allocate (1-100%)
- **Cash Amount**: Dollar value per position ($100+)
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long entries on/off
- **Enable Short Scalps**: Toggle short entries on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between signals (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for trade enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time strategy status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Color Settings**: Customize long/short/TP/SL colors
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short entries
- **Active TP/SL lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing position status, channel state, last signal, entry/TP/SL prices, and ATR status
## HOW TO USE:
### For 1-3 Minute Scalping (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars
- Position Size: 1-2 contracts
### For 5-15 Minute Day Trading:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- Position Size: Fixed contracts or 5-10% equity
### For 30-60 Minute Swing Scalping:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- Position Size: % of equity recommended
## BACKTEST CONSIDERATIONS:
- Strategy performs best in ranging, mean-reverting markets
- Strong trending markets produce more stop losses as price breaks channels
- ATR filter significantly reduces trade count but improves quality during low volatility
- Cooldown period trades signal quantity for signal quality
- Commission and slippage materially impact sub-5-minute timeframe performance
- Shorter timeframes require tighter entry zones (15-20%) to catch quick reversions
- % of Channel TP adapts better to varying channel sizes than fixed points
- Fixed contract sizing recommended for consistent risk per trade in futures
**Backtesting Parameters Used**: This strategy was developed and tested using realistic commission and slippage values to provide accurate performance expectations. Recommended settings: Commission of $1.40 per side (typical for NQ futures through discount brokers), slippage of 2 ticks to account for execution delays on fast-moving scalp entries. These values reflect real-world trading costs that active scalpers will encounter. Backtest results without proper cost simulation will significantly overstate profitability.
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features require data feed with volume information but are optional for core functionality.
## KNOWN LIMITATIONS:
- Immediate touch entry can fire multiple times in choppy zones without adequate cooldown
- Channel deletion at 10-tick breaks may be too aggressive or lenient depending on instrument tick size
- ATR filter from lower timeframes requires higher-tier TradingView subscription (request.security limitation)
- Mean reversion logic fails in strong breakout scenarios leading to stop loss hits
- Position sizing via % of equity or cash amount calculates based on close price, may differ from actual fill price
- No partial closing capability - full position exits at TP or SL only
- Strategy does not account for gap openings or overnight holds
## RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance does not guarantee future results. This strategy is for educational purposes and backtesting only. Mean reversion strategies can experience extended drawdowns during trending markets. Stop losses may not fill at intended levels during extreme volatility or gaps. Thoroughly test on historical data and paper trade before risking real capital. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Automated trading systems can malfunction - monitor all live positions actively.
## ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based signals, multi-timeframe ATR volatility filtering, flexible position sizing (fixed/percentage/cash), cooldown period filtering, dual TP methods (fixed points vs channel percentage), automated strategy execution with exit management, and real-time position monitoring table.
LANZ Strategy 6.0 [Backtest]🔷 LANZ Strategy 6.0 — Precision Backtesting Based on 09:00 NY Candle, Dynamic SL/TP, and Lot Size per Trade
LANZ Strategy 6.0 is the simulation version of the original LANZ 6.0 indicator. It executes a single LIMIT BUY order per day based on the 09:00 a.m. New York candle, using dynamic Stop Loss and Take Profit levels derived from the candle range. Position sizing is calculated automatically using capital, risk percentage, and pip value — allowing accurate trade simulation and performance tracking.
📌 This is a strategy script — It simulates real trades using strategy.entry() and strategy.exit() with full money management for risk-based backtesting.
🧠 Core Logic & Trade Conditions
🔹 BUY Signal Trigger:
At 09:00 a.m. NY (New York time), if:
The current candle is bullish (close > open)
→ A BUY order is placed at the candle’s close price (EP)
Only one signal is evaluated per day.
⚙️ Stop Loss / Take Profit Logic
SL can be:
Wick low (0%)
Or dynamically calculated using a % of the full candle range
TP is calculated using the user-defined Risk/Reward ratio (e.g., 1:4)
The TP and SL levels are passed to strategy.exit() for each trade simulation.
💰 Risk Management & Lot Size Calculation
Before placing the trade:
The system calculates pip distance from EP to SL
Computes the lot size based on:
Account capital
Risk % per trade
Pip value (auto or manual)
This ensures every trade uses consistent, scalable risk regardless of instrument.
🕒 Manual Close at 3:00 p.m. NY
If the trade is still open by 15:00 NY time, it will be closed using strategy.close().
The final result is the actual % gain/loss based on how far price moved relative to SL.
📊 Backtest Accuracy
One trade per day
LIMIT order at the candle close
SL and TP pre-defined at execution
No repainting
Session-restricted (only runs on 1H timeframe)
✅ Ideal For:
Traders who want to backtest a clean and simple daily entry system
Strategy developers seeking reproducible, high-conviction trades
Users who prefer non-repainting, session-based simulations
👨💻 Credits:
💡 Developed by: LANZ
🧠 Logic & Money Management Engine: LANZ
📈 Designed for: 1H charts
🧪 Purpose: Accurate simulation of LANZ 6.0's NY Candle Entry system
Tactical FlowTactical Flow – Altcoin Swing Strategy with Trend Logic & Dynamic TP System
(Built for 1H timeframe altcoin trading)
🎯 Purpose
Tactical Flow is a swing trading strategy purpose-built for altcoins on the 1-hour timeframe. It targets clean trend continuation setups by combining non-repainting filters for direction, momentum, and volume with a real-time execution engine that strictly avoids same-bar reversals. It includes a dynamic take-profit system with real-time trade tracking and an integrated visual dashboard.
⚙️ Strategy Core Components
Each module was chosen for precision, trend clarity, and altcoin-specific price behavior.
🔹 1. White Line Bias
Defines market structure using the midpoint of recent high/low range.
→ Keeps you trading with the dominant structure.
🔹 2. Tether Trend Engine
Two mid-range bands (Fast & Slow Tether) act like a dynamic trend cloud.
→ Ensures trend direction is confirmed with structural layering.
🔹 3. ZLEMA Gradient Filter
A Zero Lag EMA of price that’s compared to its previous value for momentum slope.
→ Confirms the trend has actual energy behind it.
🔹 4. TEMA Micro-Flow
A smoothed directional signal to confirm price is accelerating, not just trending.
→ Filters out late or fading entries.
🔹 5. Volume Spike Filter
Confirms that breakouts are real by requiring volume > 1.5× median of previous candles.
→ Designed for altcoins to avoid fakeouts during random volatility.
🔹 6. RMI Trend Memory
Keeps track of the trend state over time, allowing for smoother transitions and fewer whipsaws.
→ Helps the strategy stay in trend longer and only reverse when confirmation is strong.
🔹 7. Reversal Cooldown Logic
Exits a trade, then waits 1 full bar before taking a reversal entry.
→ Avoids common backtest false positives where entries and exits occur on the same candle.
💸 Trade Management – TP1/TP2 Logic
TP1 = 50% closed when price hits target 1
TP2 = full exit
Exits early if trend weakens
Supports dynamic reentry after TP2 if trend resumes
→ Keeps risk controlled while allowing position scaling in volatile altcoin swings.
📊 Strategy Dashboard
Visual interface shows:
Current Position (Long / Short / Flat)
Entry Price
TP1 and TP2 hit status
Bars since entry
Real-time Win Rate
Profit Factor
🧪 Backtesting & Execution Compliance
✅ Fully non-repainting
✅ Compatible with TradingView's deep backtesting
✅ Uses strategy.exit with limit logic for accurate TP tracking
✅ No stop-loss — closes trades on trend weakening only
🔥 Best Use Case
Altcoin swing trades on 1H chart
Works well during trending periods with volume
Not designed for choppy or sideways conditions
Pairs well with watchlist scanners and heatmaps
[SHORT ONLY] Internal Bar Strength (IBS) Mean Reversion Strategy█ STRATEGY DESCRIPTION
The "Internal Bar Strength (IBS) Strategy" is a mean-reversion strategy designed to identify trading opportunities based on the closing price's position within the daily price range. It enters a short position when the IBS indicates overbought conditions and exits when the IBS reaches oversold levels. This strategy is Short-Only and was designed to be used on the Daily timeframe for Stocks and ETFs.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) measures where the closing price falls within the high-low range of a bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
- Low IBS (≤ 0.2) : Indicates the close is near the bar's low, suggesting oversold conditions.
- High IBS (≥ 0.8) : Indicates the close is near the bar's high, suggesting overbought conditions.
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The IBS value rises to or above the Upper Threshold (default: 0.9).
The Closing price is greater than the previous bars High (close>high ).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
An exit Signal is generated when the IBS value drops to or below the Lower Threshold (default: 0.3). This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Upper Threshold: The IBS level at which the strategy enters trades. Default is 0.9.
Lower Threshold: The IBS level at which the strategy exits short positions. Default is 0.3.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for Stocks and ETFs markets and performs best when prices frequently revert to the mean.
The strategy can be optimized further using additional conditions such as using volume or volatility filters.
It is sensitive to extreme IBS values, which help identify potential reversals.
Backtesting results should be analyzed to optimize the Upper/Lower Thresholds for specific instruments and market conditions.
Internal Bar Strength (IBS) Strategy█ STRATEGY DESCRIPTION
The "Internal Bar Strength (IBS) Strategy" is a mean-reversion strategy designed to identify trading opportunities based on the closing price's position within the daily price range. It enters a long position when the IBS indicates oversold conditions and exits when the IBS reaches overbought levels. This strategy was designed to be used on the daily timeframe.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) measures where the closing price falls within the high-low range of a bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
- **Low IBS (≤ 0.2)**: Indicates the close is near the bar's low, suggesting oversold conditions.
- **High IBS (≥ 0.8)**: Indicates the close is near the bar's high, suggesting overbought conditions.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The IBS value drops below the Lower Threshold (default: 0.2).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the IBS value rises to or above the Upper Threshold (default: 0.8). This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Upper Threshold: The IBS level at which the strategy exits trades. Default is 0.8.
Lower Threshold: The IBS level at which the strategy enters long positions. Default is 0.2.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for ranging markets and performs best when prices frequently revert to the mean.
It is sensitive to extreme IBS values, which help identify potential reversals.
Backtesting results should be analyzed to optimize the Upper/Lower Thresholds for specific instruments and market conditions.
TDGS Dynamic Grid Trading Strategy [CoinFxPro]Advanced Dynamic Grid Trading Strategy
Logic and Working Principle:
This strategy uses a dynamic grid system to support both long and short trades. Grid trading aims to capitalize on price fluctuations within a predefined range by executing buy and sell orders systematically. The system calculates grid levels based on a base price and dynamically trades within these levels.
Grid Levels:
Grid levels are calculated based on the initial price and the user-defined grid spacing percentage.
Long Mode: Buys when the price decreases and sells when the price increases.
Short Mode: Sells when the price increases and buys when the price decreases.
Grid Updates:
Grid levels are recalculated based on the market price when the price moves by a user-defined update percentage.
For example;
In Long mode, when the price shows an upward trend, that is, when it rises by the Grid Update Percentage specified by the user, Grid levels are recreated and trades are made according to the new grid levels. While the price and grid levels are updated according to the new price, the Stop level is also updated upwards and the stop is followed with the TrailingStop logic.
In short mode, the same system operates with reverse logic. In other words, as prices decrease downwards, the grids are updated downwards when the Grid update percentage determined by the user decreases. The stop level is also updated accordingly.
The difference of the strategy from other Gridbots is that the grid levels are automatically updated and the levels are recreated with the price percentage difference determined by the user. Old levels can be tracked on the chart.
As the price updates, the self-updating grid levels are updated upwards in long mode and downwards in short mode.
The number of buying lots and selling lots are separated, allowing both trading within the position and the opportunity to collect lots and increase the position.
When trading with the grid trading logic, when buying and selling between grids, there is no repeated purchase at the same level unless there is a sale at the upper grid level. In this way, each level will be traded within itself.
For example, in a long condition, when the price is going up, after deducting the selling lot from the buying lot at each level, the remaining lots will be collected while the price is going up and an opportunity will be provided from the price rise.
Different preferences have been added to the profit taking conditions, allowing the robot to continue or stop after profit taking, if desired.
The system, which acts entirely according to user parameters, constantly updates itself as long as it moves in the direction determined by itself, and in these conditions, transactions are carried out according to profit or stop conditions.
Parameters:
Grid Parameters:
Settings such as buy lot size, sell lot size, grid count, and grid spacing percentage allow flexibility and customization.
Risk Management:
Stop loss (%) and take profit (%) levels help limit potential losses and secure profits at predefined thresholds.
Objective:
The goal of this strategy is to systematically capitalize on market price fluctuations through automated grid trading. This method is particularly effective in volatile markets where the price oscillates within a specific range.
The strategy works with a complete algorithm logic, and in appropriate instruments (especially instruments with depth and transaction volume should be preferred), buying and selling transactions are made according to the parameters determined at the beginning, and if the conditions go beyond the conditions, the stop is made, and when the profit taking conditions are met, it takes profit and prices according to the determined value. When it is updated, the values are updated again and the parameter works algorithmically.
Risk Management Recommendations:
Initial Capital: Grid trading involves frequent transactions, so sufficient initial capital is essential.
Stop Loss: Always set stop loss levels to prevent significant losses.
Grid Count and Spacing: A higher number of grids provides more trading opportunities but using grids that are too close may increase transaction costs due to small price movements.
First of all, it is important for risk management that you choose instruments that have depth and high transaction volume.
Strategy results may differ as a result of the parameters entered. Therefore, before trading in your real account, it is recommended that you start real transactions after backtesting with different parameters.
If you are stuck on something, you can mention it in the comments.
[3Commas] DCA Bot TesterDCA Bot Tester
🔷What it does: A tool designed to simulate the behavior of a Dollar Cost Averaging (DCA) strategy based on input signals from a source indicator. Additionally, it enables you to send activation signals to 3Commas Bots via TradingView webhooks.
🔷Who is it for: This tool is ideal for those who want a visual representation and strategy report of how a DCA Bot would perform under specific conditions. By adjusting the parameters, you can assess whether the strategy aligns with your risk/reward expectations before implementation, helping you save time and protect your capital.
🔷How does it work: The tool leverages a pyramiding function to simulate price averaging, mimicking how a DCA Bot operates. It calculates volume-based averaging and, upon reaching the target, closes the positions. Conversely, if the target isn't reached, a Stop Loss is triggered, potentially resulting in significant losses if improperly configured.
🔷Why It’s Unique
Easy visualization of DCA Bot entry and exit points according to user preferences.
DCA Bot Summary table same as the one shown in the new 3Commas interface.
Use plots from other indicators as Entry Trigger Source, with a small modification of the code.
Option to Review message format before sending Signals to 3Commas. Compatibility with Multi-Pair, and futures contract pairs.
Option to filter signals by session and day according to the user’s timezone.
👉 Before continuing with the explanation of the tool, please take a few minutes to read this information, paying special attention to the risks of using DCA strategies.
DCA Bot: What is it, how does it work, and what are its advantages and risks?
A DCA Bot is an automated tool designed to simplify and optimize your trading operations, particularly in cryptocurrencies. Based on the concept of Dollar Cost Averaging (DCA) , this bot implements scaled strategies that allow you to distribute your investments intelligently. The key lies in dividing your capital into multiple orders, known as base orders and safety orders, which are executed at different price levels depending on market conditions.
These bots are highly customizable, meaning you can adapt them to your goals and trading style, whether you're operating Long (expecting a price increase) or Short (expecting a price decrease). Their primary purpose is to reduce the impact of entries that move against the estimated direction and ensure you achieve a more favorable average price.
🔸 Key Features of DCA Bots
Customizable configuration: DCA bots allow you to adjust the size of your initial investment, the number of safety orders, and the price levels at which these orders execute. These orders can be equal or incremental, depending on your risk tolerance.
Scaled safety orders: If the asset's price moves against your position, the bot executes safety orders at strategic levels to average your entry price and increase your chances of closing in profit.
Automatic Take Profit: When the predefined profit level is reached, the bot closes the position, ensuring net gains by averaging all entries made using the DCA strategy.
Stop Loss option: To protect your capital, you can set a stop loss level that limits losses if the market moves drastically against your position.
Flexibility: Bots can integrate with 3Commas technical indicators or external signals from TradingView, allowing you to trade in any trend, whether bullish or bearish.
Support for multiple assets: You can trade cryptocurrency pairs and exchanges compatible with 3Commas, offering a wide range of possibilities to diversify your strategies.
✅ Advantages of DCA Bots
Time-saving automation: DCA bots eliminate the need for constant market monitoring, executing your trades automatically and efficiently based on predefined settings.
Favorable averages in volatile markets: By averaging your entries, the bot can offer more competitive prices even under adverse market conditions. This increases your chances of recovering a position and closing it profitably.
Advanced capital management: With customizable settings, you can adjust the size of base and safety orders to optimize capital usage and reduce risk.
Additional protection: The ability to set a stop loss ensures your losses are limited, safeguarding your capital in extreme scenarios.
⚠️ Risks of Using a DCA Bot
Requires significant capital: Safety orders can accumulate quickly if the price moves against your position. This issue is compounded if increasing amounts are used for safety orders, which can immobilize large portions of capital in adverse markets.
Markets lacking clear direction: During consolidation periods or erratic movements, the bot may generate unrealized losses and make position recovery difficult.
Opportunity cost: Investing in an asset that doesn't show favorable behavior can prevent you from seizing opportunities in other markets.
Emotional pressure: Large investments in advanced stages of the DCA strategy can cause stress, especially if an asset takes too long to reach your take profit level.
Dependence on market recovery: DCA assumes that the price will eventually move in your favor, which does not always happen, especially in assets without solid fundamentals.
📖 Key Considerations for Effectively Using a DCA Bot
Use small amounts for your base and safety orders: Setting small initial orders not only limits capital usage but also allows you to manage multiple bots simultaneously, maximizing portfolio diversification.
Capital management: Define a clear budget and never risk more than you are willing to lose. This is essential for maintaining sustainable operations.
Select assets with strong fundamentals: Apply DCA to assets you understand and that have solid fundamentals and a proven historical growth record. Additionally, analyze each cryptocurrency's fundamentals: What problem does it solve? Does it have a clear use case? Is it viable in the long term? These questions will help you make more informed decisions.
Diversification: Do not concentrate all your capital on a single asset or strategy. Spread your risk across multiple bots or assets.
Monitor regularly: While bots are automated and eliminate the need to monitor the market constantly, it is essential to monitor the bots themselves to ensure they are performing as expected. This includes reviewing their performance and making adjustments if market conditions change. Remember, the goal is to automate trades, but active bot management is crucial to avoid surprises.
A DCA Bot is a powerful tool for traders looking to automate their strategies and reduce the impact of market fluctuations. However, like any tool, its success depends on how it is configured and used. By applying solid capital management principles, carefully selecting assets, and using small amounts in your orders, you can maximize its potential and minimize risks.
🔷FEATURES & HOW TO USE
🔸Strategy: Here you must select the type of signal you are going to analyze and send signals to the DCA Bot, either Long for buy signals or Short for sell signals. This must match the Bot created in 3Commas.
🔸Add a Source Indicator for Entry Triggers
Tradingview allows us to use indicator plots as a source in other indicators, we will use this functionality so that the buy or sell signals of an indicator are processed by the DCA Bot Tester.
In this EXAMPLE we will use a simple strategy that uses a Donchian Channel (DC) and an Exponential Moving Average (EMA).
Trigger to buy or long signal will be when: the price closes above the previous upper level and the average of the upper and lower level (basis) is greater than the EMA.
Trigger sell or short signal will be when: the price closes below the previous lower level and the average of the upper and lower level (basis) is less than the EMA.
trigger_buy = ta.crossover (close,upper ) and basis > ema and barstate.isconfirmed
trigger_sell = ta.crossunder(close,lower ) and basis < ema and barstate.isconfirmed
Then we create the plots that will be used as input source in the DCA Bot Tester indicator.
When a buy condition is given the plot "🟢 Trigger Buy" will have a value of 1 otherwise it will remain at 0.
When a sell condition is given the plot "🔴 Trigger Sell" will have a value of -1 otherwise it will remain at 0.
plot(trigger_buy ? 1 : 0 , '🟢 Trigger Buy' , color = na, display = display.data_window)
plot(trigger_sell? -1 : 0 , '🔴 Trigger Sell', color = na, display = display.data_window)
Here you have the complete code so you can use it and do tests. Basically you just have to define the buy or sell conditions of your preferred indicator or strategy and then create the plots with the same format that will be used in DCA Bot Tester.
//@version=6
indicator(title="Simple Strategy Example", overlay= false)
// Indicator and Signal Triggers
length = input.int(10, title = "DC Length" , display = display.none)
length_ema = input.int(50, title = "EMA Length", display = display.none)
lower = ta.lowest (length)
upper = ta.highest(length)
ema = ta.ema (close, length_ema)
basis = math.avg (upper, lower)
plot(basis, "Basis", color = color.orange, display = display.all-display.status_line)
plot(upper, "Upper", color = color.blue , display = display.all-display.status_line)
plot(lower, "Lower", color = color.blue , display = display.all-display.status_line)
plot(ema , "EMA" , color = color.red , display = display.all-display.status_line)
candlecol = open < close ? color.teal : color.red
plotcandle(open, high, low, close, title='Candles', color = candlecol, wickcolor = candlecol, bordercolor = candlecol, display = display.pane)
trigger_buy = ta.crossover (close,upper ) and basis > ema and barstate.isconfirmed
trigger_sell = ta.crossunder(close,lower ) and basis < ema and barstate.isconfirmed
plotshape(trigger_buy ?close:na, title="Label Buy" , style=shape.labelup , location= location.belowbar, color=color.green, text="B", textcolor=color.white, display=display.pane)
plotshape(trigger_sell?close:na, title="Label Sell", style=shape.labeldown, location= location.abovebar, color=color.red , text="S", textcolor=color.white, display=display.pane)
// ――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――
// 👇 Plots to be used in the DCA Bot Indicator as source triggers.
// ――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――
plot(trigger_buy ? 1 : 0 , '🟢 Trigger Buy' , color = na, display = display.data_window)
plot(trigger_sell? -1 : 0 , '🔴 Trigger Sell', color = na, display = display.data_window)
To use the example code
Open the Pine Editor, paste the code and then click Add to chart.
Then in the Plot Entry Trigger Source option, we will select 🟢 Trigger Buy, as the plot that will give us the buy signals when it is worth 1, otherwise for the sell signals you must change the value to -1 in the Plot Entry Trigger Value and remember to change the strategy mode to Short.
🔸DCA Settings: Here you need to configure the DCA values of the strategy, you can see the meaning of each value in the Settings Section. Once you are satisfied with the tests configure the 3Commas DCA Bot with the same values so that the Summary Table matches the 3Commas Table. Pay close attention to the Total Volume that the Bot will use, according to the amount of Safety Orders you are going to execute, and that all the values in the table adapt to your risk tolerance.
🔸DCA Bot Deal Start: Once you create the Bot in 3Commas with the same settings it will give you a Deal Start Message, you must copy and paste it in this section, verify that it is the same in the summary table, this is used to be sent through tradingview alerts to the Bot and it can process the signals.
🔸DCA Bot Multi-Pair: A Multi-Pair Bot allows you to manage several pairs with a single bot, but you must specify which pair it will run on. 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 3Commas format) the symbol for each pair before you create the alert so that the bot understands which pair to work on.
In the following image we would be configuring the indicator to send a signal to activate the bot in the BTCUSDT pair using the given format it would be USDT_BTC, but if we wanted to send a signal in another pair we must change the pair in the chart and also in the configuration, an example with ETHUSDT would be USDT_ETH. After this we could create the alert, and the Mult-Pair Bot would detect it correctly.
🔸Strategy Tester Filters: This is useful if you want to test the strategy's result on a certain time window, the indicator will only enter this range. If disabled it will use all historical data available on the chart. If you are going to use the tool to send signals, make sure to disable the Use Custom Test Period. If you want the entries to only run at a certain time and day, in that case make sure that the timezone matches the one you are using in the chart.
🔸Properties: Adjust your initial capital and exchange commission appropriately to achieve realistic results.
🔸Create alerts to trigger the DCA Bot
Check that the message is the same as the one indicated by the DCA Bot.
In the case of Multi-Pair, enable the option to add the symbol with the correct format.
When creating an alert, select Any alert() function call.
Enter the any name of the alert.
Open the Notifications tab and enable Webhook URL
Paste Webhook URL provided by 3Commas looking in the section How to use TradingView custom signals.
Done, alerts will be sent with the correct format automatically to 3Commas.
🔷 INDICATOR SETTINGS
🔸3Commas DCA Bot Settings
Strategy: Select the direction of the strategy to test Long or Short, this must be the same as the Bot created in 3Commas, so that the signals are processed properly.
DCA Bot Deal Start: Copy and paste the message for the deal start signal of the DCA Bot you created in 3Commas. This is the message that will be sent with the alert to the Bot, you must verify that it is the same as the 3Commas bot so that it can process properly so that it executes and starts the trade.
DCA Bot Multi-Pair: A Multi-Pair Bot allows you to manage several pairs with a single bot, but you must specify which pair it will run on.
DCA Bot Summary Table: Here you can activate the display of table as well as change the size, position, text color and background color.
🔸Source Indicator Settings
Plot Entry Trigger Source: Select a Plot for Entries of the Source Indicator. This refers to the Long or Short entry signal that the indicator will use as BO (Base Order).
Plot Entry Trigger Value: Value of the Source Indicator to Deal Start Condition Trigger. The default value is 1, this means that when a signal is given for example Long in the source indicator, we will use 1 or for Short -1 if there is no signal it will be 0 so it will not execute any entry, please review the example code and adjust the indicator you are going to use in the same way.
🔸DCA Settings
Base Order: The Base Order is the first order the bot will create when starting a new deal.
Safety Order: Enter the amount of funds your safety orders will use to average the cost of the asset being traded.Safety orders are also known as Dollar Cost Averaging and help when prices move in the opposite direction to your bot's take profit target.
Safety Orders Deviation %: Enter the percentage difference in price to create the first Safety Order. All Safety Orders are calculated from the price the initial Base Order was filled on the exchange account.
Safety Orders Max Count: This is the total number of Safety Orders the bot is allowed to use per deal that is opened. All Safety Orders created by the bot are placed as Limit Orders on the exchange's order book.
Safety Orders Volume Scale: The Safety Order Volume Scale is used to multiply the amount of funds used by the last Safety Order that was created. Using a larger amount of funds for Safety Orders allows your bot to be more aggressive at Dollar Cost Averaging the price of the asset being traded.
Safety Orders Step Scale: The Safety Order Step Scale is used to multiply the Price Deviation percentage used by the last Safety Order placed on the exchange account. Using a larger value here will reduce the amount of Safety Orders your bot will require to cover a larger move in price in the opposite direction to the active deal's take profit target.
Take Profit %: The Take Profit section offers tools for flexible management of target parameters: automatic profit upon reaching one or more target levels in percentage.
Stop Loss % | Use SL: To enable Stop Loss, please check the "Use SL" box. This is the percentage that price needs to move in the opposite direction to close the deal at a loss. This must be greater than the sum of the deviations from the safety orders.
🔸Strategy Tester Filters
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.
Session Filter | Days | Background: Here you can choose a time zone in which signals will be sent or your strategy will be tested, as well as the days and a background of it. It is important that you use the same timezone as your chart so that it matches.
👨🏻💻💭 If this tool helps you, don’t forget to give it a boost! Feel free to share in the comments how you're using it or if you have any questions.
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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.
Z-Score RSI StrategyOverview
The Z-Score RSI Indicator is an experimental take on momentum analysis. By applying the Relative Strength Index (RSI) to a Z-score of price data, it measures how far prices deviate from their mean, scaled by standard deviation. This isn’t your traditional use of RSI, which is typically based on price data alone. Nevertheless, this unconventional approach can yield unique insights into market trends and potential reversals.
Theory and Interpretation
The RSI calculates the balance between average gains and losses over a set period, outputting values from 0 to 100. Typically, people look at the overbought or oversold levels to identify momentum extremes that might be likely to lead to a reversal. However, I’ve often found that RSI can be effective for trend-following when observing the crossover of its moving average with the midline or the crossover of the RSI with its own moving average. These crossovers can provide useful trend signals in various market conditions.
By combining RSI with a Z-score of price, this indicator estimates the relative strength of the price’s distance from its mean. Positive Z-score trends may signal a potential for higher-than-average prices in the near future (scaled by the standard deviation), while negative trends suggest the opposite. Essentially, when the Z-Score RSI indicates a trend, it reflects that the Z-score (the distance between the average and current price) is likely to continue moving in the trend’s direction. Generally, this signals a potential price movement, though it’s important to note that this could also occur if there’s a shift in the mean or standard deviation, rather than a meaningful change in price itself.
While the Z-Score RSI could be an insightful addition to a comprehensive trading system, it should be interpreted carefully. Mean shifts may validate the indicator’s predictions without necessarily indicating any notable price change, meaning it’s best used in tandem with other indicators or strategies.
Recommendations
Before putting this indicator to use, conduct thorough backtesting and avoid overfitting. The added parameters allow fine-tuning to fit various assets, but be careful not to optimize purely for the highest historical returns. Doing so may create an overly tailored strategy that performs well in backtests but fails in live markets. Keep it balanced and look for robust performance across multiple scenarios, as overfitting is likely to lead to disappointing real-world results.
Dual Chain StrategyDual Chain Strategy - Technical Overview
How It Works:
The Dual Chain Strategy is a unique approach to trading that utilizes Exponential Moving Averages (EMAs) across different timeframes, creating two distinct "chains" of trading signals. These chains can work independently or together, capturing both long-term trends and short-term price movements.
Chain 1 (Longer-Term Focus):
Entry Signal: The entry signal for Chain 1 is generated when the closing price crosses above the EMA calculated on a weekly timeframe. This suggests the start of a bullish trend and prompts a long position.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Exit Signal: The exit signal is triggered when the closing price crosses below the EMA on a daily timeframe, indicating a potential bearish reversal.
exitLongChain1 = enableChain1 and ta.crossunder(src1, exitEMA1)
Parameters: Chain 1's EMA length is set to 10 periods by default, with the flexibility for user adjustment to match various trading scenarios.
Chain 2 (Shorter-Term Focus):
Entry Signal: Chain 2 generates an entry signal when the closing price crosses above the EMA on a 12-hour timeframe. This setup is designed to capture quicker, shorter-term movements.
bullishChain2 = enableChain2 and ta.crossover(src2, entryEMA2)
Exit Signal: The exit signal occurs when the closing price falls below the EMA on a 9-hour timeframe, indicating the end of the shorter-term trend.
exitLongChain2 = enableChain2 and ta.crossunder(src2, exitEMA2)
Parameters: Chain 2's EMA length is set to 9 periods by default, and can be customized to better align with specific market conditions or trading strategies.
Key Features:
Dual EMA Chains: The strategy's originality shines through its dual-chain configuration, allowing traders to monitor and react to both long-term and short-term market trends. This approach is particularly powerful as it combines the strengths of trend-following with the agility of momentum trading.
Timeframe Flexibility: Users can modify the timeframes for both chains, ensuring the strategy can be tailored to different market conditions and individual trading styles. This flexibility makes it versatile for various assets and trading environments.
Independent Trade Logic: Each chain operates independently, with its own set of entry and exit rules. This allows for simultaneous or separate execution of trades based on the signals from either or both chains, providing a robust trading system that can handle different market phases.
Backtesting Period: The strategy includes a configurable backtesting period, enabling thorough performance assessment over a historical range. This feature is crucial for understanding how the strategy would have performed under different market conditions.
time_cond = time >= startDate and time <= finishDate
What It Does:
The Dual Chain Strategy offers traders a distinctive trading tool that merges two separate EMA-based systems into one cohesive framework. By integrating both long-term and short-term perspectives, the strategy enhances the ability to adapt to changing market conditions. The originality of this script lies in its innovative dual-chain design, providing traders with a unique edge by allowing them to capitalize on both significant trends and smaller, faster price movements.
Whether you aim to capture extended market trends or take advantage of more immediate price action, the Dual Chain Strategy provides a comprehensive solution with a high degree of customization and strategic depth. Its flexibility and originality make it a valuable tool for traders seeking to refine their approach to market analysis and execution.
How to Use the Dual Chain Strategy
Step 1: Access the Strategy
Add the Script: Start by adding the Dual Chain Strategy to your TradingView chart. You can do this by searching for the script by name or using the link provided.
Select the Asset: Apply the strategy to your preferred trading pair or asset, such as #BTCUSD, to see how it performs.
Step 2: Configure the Settings
Enable/Disable Chains:
The strategy is designed with two independent chains. You can choose to enable or disable each chain depending on your trading style and the market conditions.
enableChain1 = input.bool(true, title='Enable Chain 1')
enableChain2 = input.bool(true, title='Enable Chain 2')
By default, both chains are enabled. If you prefer to focus only on longer-term trends, you might disable Chain 2, or vice versa if you prefer shorter-term trades.
Set EMA Lengths:
Adjust the EMA lengths for each chain to match your trading preferences.
Chain 1: The default EMA length is 10 periods. This chain uses a weekly timeframe for entry signals and a daily timeframe for exits.
len1 = input.int(10, minval=1, title='Length Chain 1 EMA', group="Chain 1")
Chain 2: The default EMA length is 9 periods. This chain uses a 12-hour timeframe for entries and a 9-hour timeframe for exits.
len2 = input.int(9, minval=1, title='Length Chain 2 EMA', group="Chain 2")
Customize Timeframes:
You can customize the timeframes used for entry and exit signals for both chains.
Chain 1:
Entry Timeframe: Weekly
Exit Timeframe: Daily
tf1_entry = input.timeframe("W", title='Chain 1 Entry Timeframe', group="Chain 1")
tf1_exit = input.timeframe("D", title='Chain 1 Exit Timeframe', group="Chain 1")
Chain 2:
Entry Timeframe: 12 Hours
Exit Timeframe: 9 Hours
tf2_entry = input.timeframe("720", title='Chain 2 Entry Timeframe (12H)', group="Chain 2")
tf2_exit = input.timeframe("540", title='Chain 2 Exit Timeframe (9H)', group="Chain 2")
Set the Backtesting Period:
Define the period over which you want to backtest the strategy. This allows you to see how the strategy would have performed historically.
startDate = input.time(timestamp('2015-07-27'), title="StartDate")
finishDate = input.time(timestamp('2026-01-01'), title="FinishDate")
Step 3: Analyze the Signals
Understand the Entry and Exit Signals:
Buy Signals: When the price crosses above the entry EMA, the strategy generates a buy signal.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Sell Signals: When the price crosses below the exit EMA, the strategy generates a sell signal.
bearishChain2 = enableChain2 and ta.crossunder(src2, entryEMA2)
Review the Visual Indicators:
The strategy plots buy and sell signals on the chart with labels for easy identification:
BUY C1/C2 for buy signals from Chain 1 and Chain 2.
SELL C1/C2 for sell signals from Chain 1 and Chain 2.
This visual aid helps you quickly understand when and why trades are being executed.
Step 4: Optimize the Strategy
Backtest Results:
Review the strategy’s performance over the backtesting period. Look at key metrics like net profit, drawdown, and trade statistics to evaluate its effectiveness.
Adjust the EMA lengths, timeframes, and other settings to see how changes affect the strategy’s performance.
Customize for Live Trading:
Once satisfied with the backtest results, you can apply the strategy settings to live trading. Remember to continuously monitor and adjust as needed based on market conditions.
Step 5: Implement Risk Management
Use Realistic Position Sizing:
Keep your risk exposure per trade within a comfortable range, typically between 1-2% of your trading capital.
Set Alerts:
Set up alerts for buy and sell signals, so you don’t miss trading opportunities.
Paper Trade First:
Consider running the strategy in a paper trading account to understand its behavior in real market conditions before committing real capital.
This dual-layered approach offers a distinct advantage: it enables the strategy to adapt to varying market conditions by capturing both broad trends and immediate price action without one chain's activity impacting the other's decision-making process. The independence of these chains in executing transactions adds a level of sophistication and flexibility that is rarely seen in more conventional trading systems, making the Dual Chain Strategy not just unique, but a powerful tool for traders seeking to navigate complex market environments.
Price-Volume Dynamic - Strategy [presentTrading]█ Introduction and How it is Different
The "Price-Volume Dynamic - Strategy" leverages a unique blend of price action, volume analysis, and statistical z-scores to establish trading positions. This approach differentiates itself by integrating the concept of the Point of Control (POC) from volume profile analysis with price-based z-score indicators to create a dynamic trading strategy. It tailors entry and exit thresholds based on current market volatility, providing a responsive and adaptive trading method. This strategy stands out by considering both historical volatility and price trends to adjust trading decisions in real-time, enhancing its effectiveness in various market conditions.
BTCUSD 4h LS Performance
█ Strategy: How It Works – Detailed Explanation
🔶 Calculating Point of Control (POC)
The Point of Control (POC) represents the price level with the highest traded volume over a specified lookback period. It's calculated by dividing the price range into a number of rows, each representing a price level. The volume at each price level is tallied and the level with the maximum volume is designated as the POC.
🔶 Dynamic Thresholds Adjustments
The entry and exit thresholds are dynamically adjusted based on normalized volatility, which is derived from the current, minimum, and maximum ATR over a specified period. This normalization ensures that the thresholds adapt to changes in market conditions, making the strategy sensitive to shifts in market volatility.
BTCUSD local performance
█ Trade Direction
The strategy can be configured to trade in three different directions: Long, Short, or Both. This flexibility allows traders to align their trading strategy with their market outlook or risk preferences. By adjusting the `POC_tradeDirection` input, traders can selectively participate in market movements that match their trading style and objectives.
█ Usage
To deploy this strategy, traders should apply it within a trading software that supports scripting and backtesting, such as TradingView's Pine Script environment. Users can input their parameters based on their analysis of the market conditions and their risk tolerance. It is essential for traders to backtest the strategy using historical data to evaluate its performance and make necessary adjustments before applying it in live trading scenarios.
█ Default Settings
- Lookback Length: Sets the period over which the highest and lowest prices, and the volume per price level, are calculated. A higher lookback length smoothens the volatility but may delay response to recent market movements.
- Number of Rows: Determines the granularity of price levels within the price range. More rows provide a more detailed volume profile but require more computational resources.
- Entry Z-Score Threshold Base: Influences the sensitivity of the strategy to enter trades. Higher values make the strategy more conservative, requiring stronger deviation from the mean to trigger a trade.
- Exit Z-Score Threshold Base: Sets the threshold for exiting trades, with lower values allowing trades to close on smaller price retractions, thereby potentially preserving profits or reducing losses.
- Trading Direction: Allows selection between Long, Short, or Both, enabling traders to tailor the strategy to their market view or risk preferences.
Stochastic Z-Score Oscillator Strategy [TradeDots]The "Stochastic Z-Score Oscillator Strategy" represents an enhanced approach to the original "Buy Sell Strategy With Z-Score" trading strategy. Our upgraded Stochastic model incorporates an additional Stochastic Oscillator layer on top of the Z-Score statistical metrics, which bolsters the affirmation of potential price reversals.
We also revised our exit strategy to when the Z-Score revert to a level of zero. This amendment gives a much smaller drawdown, resulting in a better win-rate compared to the original version.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
Following this, the Stochastic Oscillator is utilized to affirm the Z-Score overbought and oversold indicators. This indicator operates within a 0 to 100 range, so a base adjustment to match the Z-Score scale is required. Post Stochastic Oscillator calculation, we recalibrate the figure to lie within the -4 to 4 range.
Finally, we compute the average of both the Stochastic Oscillator and Z-Score, signaling overpriced or underpriced conditions when the set threshold of positive or negative is breached.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURAUD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Stochastic Length: 14
Stochastic Smooth Period: 7
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 40%
FURTHER IMPLICATION
The Stochastic Oscillator imparts minimal impact on the current strategy. As such, it may be beneficial to adjust the weightings between the Z-Score and Stochastic Oscillator values or the scale of Stochastic Oscillator to test different performance outcomes.
Alternative momentum indicators such as Keltner Channels or RSI could also serve as robust confirmations of overbought and oversold signals when used for verification.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Hulk Grid Algorithm V2 - The Quant ScienceIt's the latest proprietary grid algorithm developed by our team. This software represents a clearer and more comprehensive modernization of the deprecated Hulk Grid Algorithm. In this new release, we have optimized the source code architecture and investment logic, which we will describe in detail below.
Overview
Hulk Grid Algorithm V2 is designed to optimize returns in sideways market conditions. In this scenario, the algorithm divides purchases with long orders at each level of the grid. Unlike a typical grid algorithm, this version applies an anti-martingale model to mitigate volatility and optimize the average entry price. Starting from the lower level, the purchase quantity is increased at each new subsequent level until reaching the upper level. The initial quantity of the first order is fixed at 0.50% of the initial capital. With each new order, the initial quantity is multiplied by a value equal to the current grid level (where 1 is the lower level and 10 is the upper level).
Example: Let's say we have an initial capital of $10,000. The initial capital for the first order would be $50 * 1 = $50, for the second order $50 * 2 = $100, for the third order $50 * 3 = $150, and so on until reaching the upper level.
All previously opened orders are closed using a percentage-based stop-loss and take-profit, calculated based on the extremes of the grid.
Set Up
As mentioned earlier, the user's goal is to analyze this strategy in markets with a lack of trend, also known as sideways markets. After identifying a price range within which the asset tends to move, the user can choose to create the grid by placing the starting price at the center of the range. This way, they can consider trading the asset, if the backtesting generates a return greater than the Buy & Hold return.
Grid Configuration
To create the grid, it's sufficient to choose the starting price during the launch phase. This level will be the center of the grid from which the upper and lower levels will be calculated. The grid levels are computed using an arithmetic method, adding and subtracting a configurable fixed amount from the user interface (Grid Step $).
Example: Let's imagine choosing 1000 as the starting price and 50 as the Grid Step ($). The upper levels will be 1000, 1050, 1100, 1150, 1200. The lower levels will be 950, 900, 850, 800, and 750.
Markets
This software can be used in all markets: stocks, indices, commodities, cryptocurrencies, ETFs, Forex, etc.
Application
With this backtesting software, is possible to analyze the strategy and search for markets where it can generate better performance than Buy & Hold returns. There are no alerts or automatic investment mechanisms, and currently, the strategy can only be executed manually.
Design
Is possible to modify the grid style and customize colors by accessing the Properties section of the user interface.
OKX: OriginalStrategy Name: Original
Automated trading strategy for OKX Exchange. The strategy sends orders through alerts and webhooks installed in them to your OKХ account for automated trading.
The strategy tries to determine the range of market movement and makes trades within this range. You can set the maximum take and stop at which the strategy will close the position. Parameter 1 is used to adjust the strategy to the symbol and timeframe you need.
Parameters for OKX signals:
Signal Token - your signal token created on OKX
Order Type - order type Limit, Market
Order Price Offset - In the case of a Limit order, how much it will be offset relative to the current price.
Investment Type - How to calculate the order volume. We recommend using percentage_investment and Amount no more than 50% with 5 leverage.
Amount - Order volume. Depends on the Investment Type field. For percentage_investment we recommend no more than 50%
Strategy parameters:
Take Profit % - The size of the maximum profit in % of the price movement, upon reaching which the strategy will close the position.
Stop Loss % - The size of the maximum loss in % of the price movement, upon reaching which the strategy will close the position.
Param 1 - Strategy parameter for adjusting to the current symbol or timeframe.
Current Backtest:
Account 1000$
Commission 0.1%
Crunchster's Turtle and Trend SystemThis is a combination of two popular systematic trading strategies - in the trend following category.
The strategy is designed for use on the daily timeframe. Specific features of this system are outlined below:
1. Two different strategies to choose from, "Trend" which is a volatility adjusted Exponential Moving Average (EMA) crossover strategy and "Breakout" which is my adaptation of the well documented "Turtle Strategy"
2. Uses advanced position sizing and risk management, usually reserved for institutional portfolio management, a proven technique utilised by Commodity Trading Advisors and Managed Futures funds (Algo/Quant funds).
"Trend" uses a fast (user defined) and slow EMA crossover, where the slow length is 5 times the fast length. The resulting signal is adjusted for the volatility of returns over a 252 lookback period, which helps to normalise the signal across different assets. The system goes long or short when it detects a new trend has formed.
"Break" uses the highest high or lowest low over a user defined lookback period to define the recent range. This is converted into a price normalised signal to allow the system to detect when a breakout occurs. The system goes long or short based off the breakout signal.
Position sizing is based on recent price volatility and the user defined annualised risk target. In essence positions are inverse volatility weighted, so larger size is opened during lower volatility and smaller size during increased volatility. Recent volatility is calculated as the standard deviation of returns with 14 period lookback, then extrapolated into an annualised volatility of expected returns. Annualised recent volatility is then referenced to the risk target set by the user to adjust the position size. The default settings are a conservative 15% annual risk target/volatility. Initial capital should be set as the maximum risk capital per trade (ie if $10,000 total capital and 10% risk per trade, initial capital should be $1000). Maximum leverage per position can be set independently, to facilitate hitting risk targets that are greater than the natural volatility of the traded asset, and to accommodate low volatility conditions, whilst maintaining overall risk controls. Direction (long or short) is at the user's discretion.
Hard stop losses are based on multiples of the average true range of recent price (14 period lookback), user configurable.
Strategy trailing stops are based off recent highest highs or lowest lows (user defined lookback) to cut the position if the trend or momentum is lost.
Although both strategies can be run simultaneously, optimal diversification will be achieved if ran separately/individually to avoid masking of entries.
Simple Bollinger Bands Strategy [JoseMetal]============
ENGLISH
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- Description:
This is a simple strategy based on Bollinger Bands found in "journeymaninvestor.com" by "nealosis" in 15 feb 2021, original strategy and credits to him.
The original strategy:
- Bollinger Bands, 20 length simple moving average and 2 standard deviations.
- Buy when the closing price crosses the lower band up.
- Exit trade when the closing price touches the upper band.
This strategy worked on mayor indices such as SP500 before the current economic crisis, because those indices just retrace to continue up and up. That's why after testing it on the current data and other markets the strategy is NO longer viable and I made some optimizations to it.
The modified strategy:
- Now you can LONG and SHORT, not only LONG, but depending on the asset (mainly bullish like BTC or Indices) is better to just BUY.
- You can customize BB length and deviation, a deviation of 1.5 triggers more trades and is usually better.
- Averaging added, by default you can have up to 7 positions at 1% capital each, but all is customizable.
Other extra stuff:
- Able to pick a date range.
- Able to pick % of capital used on each trade.
- Able to close trades ON PROFIT ONLY.
- Able to increase position ONLY if the price is a certain percentage better than your average.
- Able to pick a leverage.
- Visual:
Bollinger Bands are shown.
On LONG entries you get a green background color, red for SHORTs, olive to close LONG and orange to close SHORT.
Position entries/exists with contract size are shown by TradingView by default as usual.
- Customization:
Everything is customizable, from date range to BB colors.
- Usage and recommendations:
Works better on bigger timeframes, daily is the best.
Enjoy!
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INGLÉS
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- Descripción:
Esta es una simple estrategia basada en las Bandas de Bollinger encontrada en "journeymaninvestor.com" por "nealosis" en el 15 de febrero de 2021, estrategia original y créditos a él.
La estrategia original:
- Bandas de Bollinger, media móvil simple de 20 y 2 desviaciones estándar.
- Comprar cuando el precio de cierre cruza la banda inferior hacia arriba.
- Salir de la posición cuando el precio de cierre toca la banda superior.
Esta estrategia funcionaba en índices mayores como el SP500 antes de la crisis económica actual, porque esos índices sólo retroceden para seguir subiendo y subiendo. Por eso después de probarla con los datos actuales y otros mercados la estrategia ya NO es viable y le hice algunas optimizaciones.
La estrategia modificada:
- Ahora puedes operar LONG y SHORT, no solo LONG, pero dependiendo del activo (principalmente alcistas como BTC o Índices) es mejor solo COMPRAR.
- Se puede personalizar la longitud de BB y la desviación, una desviación de 1,5 desencadena más operaciones y suele ser mejor.
- Promedio añadido, por defecto puedes tener hasta 7 posiciones al 1% de capital cada una, pero todo es personalizable.
Otras cosas extra:
- Posibilidad de elegir un rango de fechas.
- Posibilidad de elegir el % de capital utilizado en cada operación.
- Posibilidad de cerrar operaciones SÓLO CON BENEFICIO.
- Posibilidad de aumentar la posición sólo si el precio es un cierto porcentaje mejor que su promedio.
- Posibilidad de elegir un apalancamiento.
- Visual:
Se muestran las Bandas de Bollinger.
En las entradas de LARGO se obtiene un color de fondo verde, rojo para CORTO, oliva para cerrar LARGO y naranja para cerrar CORTO.
Las entradas/existencias de posiciones con tamaño de contrato son mostradas por TradingView por defecto como es habitual.
- Personalización:
Todo es personalizable, desde el rango de fechas hasta los colores de BB.
- Uso y recomendaciones:
Funciona mejor en marcos de tiempo más grandes, diario es el mejor.
¡Que lo disfrutes!
BBWAS StrategyA breakout in trading refers to a situation where the price of a security or asset moves beyond a defined level of support or resistance, which is typically indicated by technical analysis tools like Bollinger Bands . Bollinger Bands consist of three lines: the upper band, the lower band, and the middle band (or basis). The upper and lower bands are set at a specified number of standard deviations away from the middle band, and they help to define the range within which the price of an asset is expected to fluctuate.
When the price of the asset moves beyond the upper or lower band, it is said to have "broken out" of the range. If the price closes below the lower band, it is considered a bearish breakout, and if it closes above the upper band, it is considered a bullish breakout.
Once a breakout occurs, traders may look for a confirmation signal before entering a trade. In this case, crossing the middle line (or basis) after a breakout may signal a potential trend reversal and a good opportunity to enter a long or short trade, depending on the direction of the breakout.
Overall, this script provides a customizable and flexible system for traders to use Bollinger Bands to identify breakout trades, with additional features to incorporate volume and RSI divergence. The dynamic TPSL system also allows traders to manage their risk and reward by automatically setting take-profit and stop-loss levels based on the volatility of the market.
Dear traders, while we strive to provide you with the best trading tools and resources, we want to remind you to exercise caution and diligence in your investing decisions.
It is important to always do your own research and analysis before making any trades. Remember, the responsibility for your investments ultimately lies with you.
Happy trading!
Strategy Myth-Busting #5 - POKI+GTREND+ADX - [MYN]This is part of a new series we are calling "Strategy Myth-Busting" where we take open public manual trading strategies and automate them. The goal is to not only validate the authenticity of the claims but to provide an automated version for traders who wish to trade autonomously.
Our fifth one we are automating is one of the strategies from "The Best 3 Buy And Sell Indicators on Tradingview + Confirmation Indicators ( The Golden Ones ))" from "Online Trading Signals (Scalping Channel)". No formal backtesting was done by them and resuructo messaged me asking if we could validate their claims.
Originally, we mimic verbatim the settings Online Trading Signals was using however weren't getting promising results. So before we stopped there we thought we might want to see if this could be improved on. So we adjusted the Renko Assignment modifier from ATR to Traditional and adjusted the value to be higher from 30 to 47. We also decided to try adding another signal confirmation to eliminate some of the ranged market conditions so we choose our favorite, ADX . Also, given we are using this on a higher time-frame we adjusted the G-Channel Trend detection source from close to OHLC4 to get better average price action indication and more accurate trend direction.
This strategy uses a combination of 2 open-source public indicators:
poki buy and sell Take profit and stop loss by RafaelZioni
G-Channel Trend Detection by jaggedsoft
Trading Rules
15m - 4h timeframe. We saw best results at the recommended 1 hour timeframe.
Long Entry:
When POKI triggers a buy signal
When G-Channel Trend Detection is in an upward trend (Green)
ADX Is above 25
Short Entry:
When POKI triggers a sell signal
When G-Channel Trend Detection is in an downward trend (red)
ADX Is above 25
If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
ATR_RSI_Strategy v2 with no repaint [liwei666]🎲 Overview
🎯 this is a optimized version based on ATR_RSI_Strategy with no-repaint.
Sharpe ratio: 1.4, trade times: 116 ,
trade symbol: BINANCE:BTCUSDTPERP 15M
you can get same backtesting result with the correct settings.
🎲 Strategy Logic
🎯 the core logic is quite simple, use ATR and RSI and SMA
1. when price is in high volatility ( atr_value > atr_ma);
2. wait for a break signal (rsi_value > rsi_buy or rsi_value < rsi_sell);
3. entry Long or Short,use trailing stop-loss to max security and percent TP to keep profit.
🎲 Settings
🎯 there are 7 input properties in script, but I only finetune 4 of them ( bold field below ),
you may change other parameter to get better result by yourself.
atr_length: length to get atr value
atr_ma_length : length of smoothing atr value
atr_ma_norm_min : atr_ma normalized min value, filter high volatility ranges
atr_ma_norm_max : atr_ma normalized max value, filter high volatility ranges
rsi_length : length to get rsi value
rsi_entry: 50 +/- rsi_entry to get entry threshold
trailing_percent: trailing stop-loss percent
🎲 Usage
🎯 the commission set to 0.05% , part of exchange the commission is less than 0.05% in reality,
but I will still use 0.05% in my next script.
🎯 this script use 50% of equity to size positions follow general script position,
you can adjust the value to fix size or 100% of equity to compare result with other strategy,
but I still suggest you use 5-10% of equity for each strategy in reality.
🎯any questions please comment below. if there are any words violate House Rule, please tell me below and i will revise immediately
don't want be hiddened again 😂😂
Additionally, I plan to publish 20 profitable strategies in 2023;
let‘s witness it together!
Hope this strategy will be usefull for you :)
enjoy! 🚀🚀🚀
RSI and MA with Trailing Stop Loss and Take Profit (by Coinrule)The relative strength index is a momentum indicator used in technical analysis. It measures the speed and magnitude of a coin's recent price changes to evaluate overvalued or undervalued conditions in the price of that coin. The RSI is displayed as an oscillator (a line graph essentially) on a scale of zero to 100. When the RSI reaches oversold levels, it can provide a signal to go long. When the RSI reaches overbought levels, it can mark a good exit point or alternatively, an entry for a short position. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
A moving average (MA) calculates the average of a selected range of prices, usually closing prices, by the number of periods in that range. Essentially it is used to help smooth out price data by creating a constantly updated average price.
The Strategy enters and closes trades when the following conditions are met:
Entry Conditions:
RSI is greater than 50
MA9 is greater than MA50
RSI increases by 5
Exit Conditions:
Price increases by 1% trailing
Price decreases by 2% trailing
This strategy is back-tested from 1 January 2022 to simulate how the strategy would work in a bear market. The strategy provides good returns.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
Simple RSI and SMA Long and Short (by Coinrule)The relative strength index ( RSI ) is a momentum indicator used in technical analysis . RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. The RSI is displayed as an oscillator (a line graph) on a scale of zero to 100. The RSI can do more than point to overbought and oversold securities. It can also indicate securities that may be primed for a trend reversal or corrective pullback in price. It can signal when to buy and sell. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
A simple moving average ( SMA ) calculates the average of a selected range of prices, usually closing prices, by the number of periods in that range.
The Strategy enters and closes the trade when the following conditions are met:
LONG
SMA100 is greater than SMA150
RSI is greater than 50
SHORT
SMA100 is less than SMA150
RSI is less than 50
When a long position is opened, it remains open until the conditions for a short are met at which point the long position is closed and the short position is opened. Then, when the conditions for the long position are met, the short will be closed and a long will be opened.
This strategy is back tested from 1 January 2022 to simulate how the strategy would work in a bear market. The strategy provides good returns.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
I11L Long Put/Call Ratio InversionThe Strategy uses the Put/Call Options Ratio Inversaion as a Signal and Implements simple Money Management rules.
It is adjusted for the NDX and for the SPX in the 30min Range.
The Oscilation has to be finetuned to reflect the correct Reversal point.
A good indicator of the correct Reversal Point is a solid range of good backtesting results..
Road To DubaiROAD TO DUBAI
Useful for daily trading over all type of asset, from Stock to Crypto, Forex and Commodities. It works best with 5min to 1hr graphs, if you are a intraday trader.
This is not a simple mashup of indicators, because you can add them as your own.
This script is more like a tool to understand price action based on indicators position . Thanks to cross call based on MACD , RSI with EMA applied and few index realtime mapping, this tool will let you reduce time effort for graph analysis .
As extra feature it will let you to try different strategies , all fully customizable.
I've tried my best to keep it readble, and easy to use. The best way to learn to use it, is to disable all features from configuration and try one by one.
CONFIGURATION TIPS : Click "Settings Gear" in the Upper Right Corner and disable "Indicator Arguments"
HOW TO PROPER SETUP
Road to Dubai 3 is semiautomatic on finding best Long and Short areas, and plot on the chart.
From configuration menu you can set a Backtrace period and sensibility for RSI EMA10, RSI EMA80 and MACD on your Timeframe, 5min, 30min, 60min.
Usually when configured, those parameters works fine on almost every asset.
You only need to start understanding signals.
STANDARD FEATURES
VWAP : Green/Red line. It will reset everyday at 00.00.
EMA80 : White Line
BLUELINES : Positive and negative overextend value from VWap . This is based on a range of bar and it will extend on the opposite side the lower or higher candle. Useful for understading where price can arrive, expecially if a spike will appear.
Those indicators are quite useful for understading trends, price positions and maximum price range.
RSI EMA10 OVERBOUGHT / OVERSOLD
Yellow arrow marks where RSI arrived at his Top or Bottom. If on different timeframes (5min, 30min and 60min) something similar happen area is filled with Red or Green.
This is base on EMA10 applied to RSI (I usually refer at it as Yellow Line on my indicator HighFreq Trader)
To find good values please try High Freq Trader 1.3
RSI EMA80 CALL
Red Cross or Green Square advice for a really potential inversion of trend. When a Silver bar appear, this means the same call was triggered on different Timeframe in the sametime.
This is based on EMA80 applied to RSI (I usually refer at it as Blue Line on my indicator HighFreq Trader).
To find good values please try High Freq Trader 1.3
MACD CALL
Based on MACD with standard settings. When triggered, a lime Triangle appears. Differents size based on timeframe (5min smaller, 60min bigger). If the same call is triggered on the same place a Lime Bar appear on the opposite side of trend (this is a graphical contents, bacause with all enabled, standard use, can be difficult to read signals).
In Menu Settings you will be able to set your best parameter for your asset.
MACD FIBONACCI EXTRA FEATURE
If you want you can enable a Fibonacci draw based on MACD . This works at his best (on my opinion) with 30min MACD
EXAMPLE
NATURAL GAS
In this chart 30min you can see all calls triggered for a Short. Yellow RSI Arrow, Red Cross, Macd Triangle and Colored Red, Lime and Silver Bars are all calling for Short.
In this way you can see in notime if this can be a perfect moment for take position
VIX VXN DXY CALLS
If VIX , VXN is triggered a small Green Dot will appear. If both are in the same time a bigger Dot appear. Very useful to find trend inversion.
If DXY is triggered a Red Dot will appear (only on Daily Chart ). Very Useful to understand trend inversion on whole market.
VOLUMES REMINDERS
Find if there was an High Volume traded (HV) or Low Volume Traded (LV) in the near past. Useful to understand if some tricky situation could happen (like a sudden sell, an accumulation or distribution)
