Outside Bar Strategy % (Alessio)Outside Bar Strategy %
This strategy is based on identifying Outside Bars, which occur when the current bar's high is higher than the previous bar's high and its low is lower than the previous bar's low. The strategy enters trades in the direction of the Outside Bar, offering a powerful way to capture price moves following a strong price expansion.
Key Features:
Long and Short Entries: The strategy enters a Long trade when the Outside Bar closes bullish (current close > open), and a Short trade when the Outside Bar closes bearish (current close < open).
Customizable Entry Levels: The entry point is calculated based on a customizable percentage of the Outside Bar's range, allowing flexibility for traders to fine-tune their entries at 50% or 70% of the bar's range.
Stop Loss (SL) and Take Profit (TP):
Stop Loss (SL) is automatically placed at the Outside Bar's low for Long trades and at its high for Short trades.
Take Profit (TP) is calculated as a percentage of the Outside Bar's range, with customizable settings for take-profit levels.
Visual Indicators:
Entry, Stop Loss, and Take Profit levels are plotted as lines on the chart, with customizable colors and widths for easy identification.
Labels are placed on the chart to indicate whether the trade is Long or Short, positioned above or below the Outside Bar's candlestick.
Alerts: Users can enable alerts to receive notifications when a trade is triggered, including details such as entry points and stop loss levels.
Strategy Parameters:
Entry Percentage: Set the entry level as a percentage of the Outside Bar's range (e.g., 50%, 70%).
Take Profit Percentage: Customize the Take Profit level as a percentage of the Outside Bar's range.
Customizable Colors and Line Widths: Adjust the colors and thickness of the entry, stop loss, and take profit lines to fit your preferences.
Alerts: Enable alerts to be notified when a trade is executed or when the entry level is reached.
This strategy is ideal for traders who want to capitalize on significant price moves after a breakout, with clear risk management through Stop Loss and Take Profit levels. The customizable features make it suitable for various market conditions and trading styles.
Pine實用程式
ema 59 Giải thích mã:
Thêm biến buyLabel và sellLabel:
buyLabel và sellLabel được sử dụng để xác định khi nào label Buy hoặc Sell xuất hiện trên biểu đồ.
Cảnh báo khi label Buy xuất hiện:
Sử dụng alertcondition với điều kiện outsideBarCrossAbove để thông báo khi label Buy xuất hiện.
Cảnh báo khi label Sell xuất hiện:
Sử dụng alertcondition với điều kiện outsideBarCrossBelow để thông báo khi label Sell xuất hiện.
Thông báo chi tiết:
Thông báo sẽ hiển thị giá tại thời điểm label xuất hiện.
Ví dụ thông báo:
Khi label Buy xuất hiện: "Label Buy xuất hiện tại giá: 1.1050".
Khi label Sell xuất hiện: "Label Sell xuất hiện tại giá: 1.1000".
Lưu ý:
Đảm bảo rằng bạn đã bật cảnh báo trong TradingView để nhận thông báo khi label xuất hiện.
Kiểm tra kỹ lưỡng chiến lược trên dữ liệu lịch sử trước khi sử dụng trong giao dịch thực tế.
Nếu bạn muốn thông báo chi tiết hơn (ví dụ: thêm thông tin về số lượng hợp đồng hoặc vị thế), bạn có thể sử dụng alert trong strategy thay vì alertcondition. Tuy nhiên, alert chỉ hoạt động với strategy, không hoạt động với indicator.
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Lỗi "Void expression cannot be assigned to a variable" xảy ra khi bạn cố gắng gán một biểu thức không trả về giá trị (void) vào một biến. Trong Pine Script, hàm plotshape không trả về giá trị nào, vì vậy bạn không thể gán nó vào một biến như buyLabel hoặc sellLabel.
Nguyên nhân:
Bạn đang cố gắng gán kết quả của plotshape vào biến buyLabel và sellLabel, nhưng plotshape không trả về giá trị.
Cách sửa lỗi:
Bạn không cần gán plotshape vào biến. Thay vào đó, bạn có thể sử dụng trực tiếp điều kiện outsideBarCrossAbove và outsideBarCrossBelow để tạo cảnh báo.
Dưới đây là đoạn mã đã được sửa:
pinescript
Copy
//@version=5
strategy(title="Outside Bar EMA Crossover Strategy with EMA Shift", shorttitle="Outside Bar EMA Cross", overlay=true)
// Input for EMA lengths
lenEMA1 = input.int(5, title="EMA 5 Length")
lenEMA2 = input.int(9, title="EMA 9 Length")
// Input for EMA 9 shift
emaShift = input.int(1, title="EMA 9 Shift", minval=0)
// Calculate EMAs
ema1 = ta.ema(close, lenEMA1)
ema2 = ta.ema(close, lenEMA2)
// Apply shift to EMA 9
ema2Shifted = na(ema2 ) ? na : ema2 // Dịch chuyển EMA 9 bằng cách sử dụng offset
// Plot EMAs
plot(ema1, title="EMA 5", color=color.blue, linewidth=2)
plot(ema2Shifted, title="EMA 9 Shifted", color=color.red, linewidth=2)
// Outside Bar condition
outsideBar() => high > high and low < low
// Cross above EMA 5 and EMA 9 (shifted)
crossAboveEMA = close > ema1 and close > ema2Shifted
// Cross below EMA 5 and EMA 9 (shifted)
crossBelowEMA = close < ema1 and close < ema2Shifted
// Outside Bar cross above EMA 5 and EMA 9 (shifted)
outsideBarCrossAbove = outsideBar() and crossAboveEMA
// Outside Bar cross below EMA 5 and EMA 9 (shifted)
outsideBarCrossBelow = outsideBar() and crossBelowEMA
// Plot shapes for visual signals
plotshape(series=outsideBarCrossAbove, title="Outside Bar Cross Above", location=location.belowbar, color=color.green, style=shape.labelup, text="Buy", textcolor=color.white)
plotshape(series=outsideBarCrossBelow, title="Outside Bar Cross Below", location=location.abovebar, color=color.red, style=shape.labeldown, text="Sell", textcolor=color.white)
// Calculate Outside Bar height
outsideBarHeight = high - low // Chiều cao của nến Outside Bar
// Calculate TP and SL levels
tpRatio = 0.5 // TP = 50% chiều cao nến Outside Bar
slRatio = 1.0 // SL = 100% chiều cao nến Outside Bar
tpLevelLong = close + outsideBarHeight * tpRatio // TP cho lệnh mua
slLevelLong = close - outsideBarHeight * slRatio // SL cho lệnh mua
tpLevelShort = close - outsideBarHeight * tpRatio // TP cho lệnh bán
slLevelShort = close + outsideBarHeight * slRatio // SL cho lệnh bán
// Strategy logic
if (outsideBarCrossAbove)
strategy.entry("Buy", strategy.long)
strategy.exit("Take Profit/Stop Loss", "Buy", stop=slLevelLong, limit=tpLevelLong) // Thêm TP và SL
if (outsideBarCrossBelow)
strategy.entry("Sell", strategy.short)
strategy.exit("Take Profit/Stop Loss", "Sell", stop=slLevelShort, limit=tpLevelShort) // Thêm TP và SL
// Logic: Nếu lệnh Buy bị Stop Loss => Vào lệnh Sell
if (strategy.position_size > 0 and close <= slLevelLong)
strategy.close("Buy")
strategy.entry("Sell After Buy SL", strategy.short)
// Logic: Nếu lệnh Sell bị Stop Loss => Vào lệnh Buy
if (strategy.position_size < 0 and close >= slLevelShort)
strategy.close("Sell")
strategy.entry("Buy After Sell SL", strategy.long)
// Cảnh báo khi label Buy xuất hiện
alertcondition(condition=outsideBarCrossAbove, title="Label Buy Xuất Hiện", message="Label Buy xuất hiện tại giá: {{close}}")
// Cảnh báo khi label Sell xuất hiện
alertcondition(condition=outsideBarCrossBelow, title="Label Sell Xuất Hiện", message="Label Sell xuất hiện tại giá: {{close}}")
Giải thích sửa lỗi:
Loại bỏ gán plotshape vào biến:
Bạn không cần gán plotshape vào biến buyLabel hoặc sellLabel. Thay vào đó, bạn có thể sử dụng trực tiếp điều kiện outsideBarCrossAbove và outsideBarCrossBelow để tạo cảnh báo.
Sử dụng alertcondition:
alertcondition được sử dụng để tạo cảnh báo khi điều kiện outsideBarCrossAbove hoặc outsideBarCrossBelow được đáp ứng.
Kết quả:
Khi label Buy xuất hiện, cảnh báo "Label Buy xuất hiện tại giá: X" sẽ được kích hoạt.
Khi label Sell xuất hiện, cảnh báo "Label Sell xuất hiện tại giá: X" sẽ được kích hoạt.
Lưu ý:
Đảm bảo rằng bạn đã bật cảnh báo trong TradingView để nhận thông báo khi label xuất hiện.
Kiểm tra kỹ lưỡng chiến lược trên dữ liệu lịch sử trước khi sử dụng trong giao dịch thực tế.
New chat
nifty supertrend tritonTrend based Strategy based on EMA , ATR and supertrend . Currently being used and testing on Nifty and Banknifty with adjusted parameters .
Do backtest before taking any trade
Fibonacci Retracement Strategy for CryptoThe Enhanced Fibonacci Retracement Strategy is designed to help traders capitalize on key Fibonacci levels for both long and short trades. This script automatically identifies significant swing highs and lows within a customizable lookback period and dynamically plots Fibonacci retracement levels (0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, and 100%) as support and resistance levels.
Key Features:
Automatic Fibonacci Levels:
The script identifies the highest high and lowest low over a user-defined lookback period to calculate Fibonacci retracement levels.
Dual-Directional Trading:
Long Trades: Triggered when the price crosses above the 61.8% retracement level, anticipating a reversal.
Short Trades: Triggered when the price crosses below the 38.2% retracement level, capturing potential downward movement.
Compact Line Option:
Users can toggle "Compact Fibonacci Lines" to reduce visual clutter on the chart, making the lines shorter and easier to interpret.
Dynamic Alerts:
Alerts are embedded directly into the strategy logic for entry and exit points.
Long Entry: Triggered when the price bounces above the 61.8% level.
Long Exit: Triggered when the price reaches the 23.6% level.
Short Entry: Triggered when the price crosses below the 38.2% level.
Short Exit: Triggered when the price reaches the 78.6% level.
Clear Visualization:
Fibonacci levels are plotted with distinct colors and dashed lines (optional compact view),
providing traders with clear and actionable levels to make decisions.
Inputs:
Lookback Period: Number of candles to calculate swing highs and lows.
Plot Fibonacci Levels: Toggle to enable/disable plotting levels.
Compact Fibonacci Lines: Reduce the length of Fibonacci lines for a cleaner chart.
How It Works:
The strategy identifies a high-low range within the lookback period.
Fibonacci levels are calculated based on the range and plotted on the chart.
Long Trade Example:
Enter when the price crosses above the 61.8% level.
Exit when the price reaches the 23.6% level.
Short Trade Example:
Enter when the price crosses below the 38.2% level.
Exit when the price reaches the 78.6% level.
Best Use Cases:
Trending Markets: Use retracements to time entries in the direction of the trend.
Range-Bound Markets: Identify and trade reversals near key Fibonacci levels.
Important Notes:
This strategy is not financial advice and should be backtested thoroughly before live trading.
Risk management is crucial! Consider using stop-loss orders for protection.
Customize inputs to suit your preferred timeframe and trading style.
Supertrend StrategyThe **Supertrend Indicator** is a popular trading tool used to identify market trends and potential buy or sell signals. It overlays on price charts and is derived from the Average True Range (ATR). By adjusting its sensitivity through the ATR multiplier and period settings, traders can use it to capture trends effectively.
- **Buy Signal**: Generated when the price moves above the Supertrend line, turning it green.
- **Sell Signal**: Occurs when the price drops below the line, turning it red.
The Supertrend is particularly useful in trending markets but may produce false signals in choppy conditions. It is often combined with other indicators for confirmation.
R-based Strategy Template [Daveatt]Have you ever wondered how to properly track your trading performance based on risk rather than just profits?
This template solves that problem by implementing R-multiple tracking directly in TradingView's strategy tester.
This script is a tool that you must update with your own trading entry logic.
Quick notes
Before we dive in, I want to be clear: this is a template focused on R-multiple calculation and visualization.
I'm using a basic RSI strategy with dummy values just to demonstrate how the R tracking works. The actual trading signals aren't important here - you should replace them with your own strategy logic.
R multiple logic
Let's talk about what R-multiple means in practice.
Think of R as your initial risk per trade.
For instance, if you have a $10,000 account and you're risking 1% per trade, your 1R would be $100.
A trade that makes twice your risk would be +2R ($200), while hitting your stop loss would be -1R (-$100).
This way of measuring makes it much easier to evaluate your strategy's performance regardless of account size.
Whenever the SL is hit, we lose -1R
Proof showing the strategy tester whenever the SL is hit: i.imgur.com
The magic happens in how we calculate position sizes.
The script automatically determines the right position size to risk exactly your specified percentage on each trade.
This is done through a simple but powerful calculation:
risk_amount = (strategy.equity * (risk_per_trade_percent / 100))
sl_distance = math.abs(entry_price - sl_price)
position_size = risk_amount / (sl_distance * syminfo.pointvalue)
Limitations with lower timeframe gaps
This ensures that if your stop loss gets hit, you'll lose exactly the amount you intended to risk. No more, no less.
Well, could be more or less actually ... let's assume you're trading futures on a 15-minute chart but in the 1-minute chart there is a gap ... then your 15 minute SL won't get filled and you'll likely to not lose exactly -1R
This is annoying but it can't be fixed - and that's how trading works anyway.
Features
The template gives you flexibility in how you set your stop losses. You can use fixed points, ATR-based stops, percentage-based stops, or even tick-based stops.
Regardless of which method you choose, the position sizing will automatically adjust to maintain your desired risk per trade.
To help you track performance, I've added a comprehensive statistics table in the top right corner of your chart.
It shows you everything you need to know about your strategy's performance in terms of R-multiples: how many R you've won or lost, your win rate, average R per trade, and even your longest winning and losing streaks.
Happy trading!
And remember, measuring your performance in R-multiples is one of the most classical ways to evaluate and improve your trading strategies.
Daveatt
Trend Following Strategy with KNN
### 1. Strategy Features
This strategy combines the K-Nearest Neighbors (KNN) algorithm with a trend-following strategy to predict future price movements by analyzing historical price data. Here are the main features of the strategy:
1. **Dynamic Parameter Adjustment**: Uses the KNN algorithm to dynamically adjust parameters of the trend-following strategy, such as moving average length and channel length, to adapt to market changes.
2. **Trend Following**: Captures market trends using moving averages and price channels to generate buy and sell signals.
3. **Multi-Factor Analysis**: Combines the KNN algorithm with moving averages to comprehensively analyze the impact of multiple factors, improving the accuracy of trading signals.
4. **High Adaptability**: Automatically adjusts parameters using the KNN algorithm, allowing the strategy to adapt to different market environments and asset types.
### 2. Simple Introduction to the KNN Algorithm
The K-Nearest Neighbors (KNN) algorithm is a simple and intuitive machine learning algorithm primarily used for classification and regression problems. Here are the basic concepts of the KNN algorithm:
1. **Non-Parametric Model**: KNN is a non-parametric algorithm, meaning it does not make any assumptions about the data distribution. Instead, it directly uses training data for predictions.
2. **Instance-Based Learning**: KNN is an instance-based learning method that uses training data directly for predictions, rather than generating a model through a training process.
3. **Distance Metrics**: The core of the KNN algorithm is calculating the distance between data points. Common distance metrics include Euclidean distance, Manhattan distance, and Minkowski distance.
4. **Neighbor Selection**: For each test data point, the KNN algorithm finds the K nearest neighbors in the training dataset.
5. **Classification and Regression**: In classification problems, KNN determines the class of a test data point through a voting mechanism. In regression problems, KNN predicts the value of a test data point by calculating the average of the K nearest neighbors.
### 3. Applications of the KNN Algorithm in Quantitative Trading Strategies
The KNN algorithm can be applied to various quantitative trading strategies. Here are some common use cases:
1. **Trend-Following Strategies**: KNN can be used to identify market trends, helping traders capture the beginning and end of trends.
2. **Mean Reversion Strategies**: In mean reversion strategies, KNN can be used to identify price deviations from the mean.
3. **Arbitrage Strategies**: In arbitrage strategies, KNN can be used to identify price discrepancies between different markets or assets.
4. **High-Frequency Trading Strategies**: In high-frequency trading strategies, KNN can be used to quickly identify market anomalies, such as price spikes or volume anomalies.
5. **Event-Driven Strategies**: In event-driven strategies, KNN can be used to identify the impact of market events.
6. **Multi-Factor Strategies**: In multi-factor strategies, KNN can be used to comprehensively analyze the impact of multiple factors.
### 4. Final Considerations
1. **Computational Efficiency**: The KNN algorithm may face computational efficiency issues with large datasets, especially in real-time trading. Optimize the code to reduce access to historical data and improve computational efficiency.
2. **Parameter Selection**: The choice of K value significantly affects the performance of the KNN algorithm. Use cross-validation or other methods to select the optimal K value.
3. **Data Standardization**: KNN is sensitive to data standardization and feature selection. Standardize the data to ensure equal weighting of different features.
4. **Noisy Data**: KNN is sensitive to noisy data, which can lead to overfitting. Preprocess the data to remove noise.
5. **Market Environment**: The effectiveness of the KNN algorithm may be influenced by market conditions. Combine it with other technical indicators and fundamental analysis to enhance the robustness of the strategy.
FTMO Rules MonitorFTMO Rules Monitor: Stay on Track with Your FTMO Challenge Goals
TLDR; You can test with this template whether your strategy for one asset would pass the FTMO challenges step 1 then step 2, then with real money conditions.
Passing a prop firm challenge is ... challenging.
I believe a toolkit allowing to test in minutes whether a strategy would have passed a prop firm challenge in the past could be very powerful.
The FTMO Rules Monitor is designed to help you stay within FTMO’s strict risk management guidelines directly on your chart. Whether you’re aiming for the $10,000 or the $200,000 account challenge, this tool provides real-time tracking of your performance against FTMO’s rules to ensure you don’t accidentally breach any limits.
NOTES
The connected indicator for this post doesn't matter.
It's just a dummy double supertrends (see below)
The strategy results for this script post does not matter as I'm posting a FTMO rules template on which you can connect any indicator/strategy.
//@version=5
indicator("Supertrends", overlay=true)
// Supertrend 1 Parameters
var string ST1 = "Supertrend 1 Settings"
st1_atrPeriod = input.int(10, "ATR Period", minval=1, maxval=50, group=ST1)
st1_factor = input.float(2, "Factor", minval=0.5, maxval=10, step=0.5, group=ST1)
// Supertrend 2 Parameters
var string ST2 = "Supertrend 2 Settings"
st2_atrPeriod = input.int(14, "ATR Period", minval=1, maxval=50, group=ST2)
st2_factor = input.float(3, "Factor", minval=0.5, maxval=10, step=0.5, group=ST2)
// Calculate Supertrends
= ta.supertrend(st1_factor, st1_atrPeriod)
= ta.supertrend(st2_factor, st2_atrPeriod)
// Entry conditions
longCondition = direction1 == -1 and direction2 == -1 and direction1 == 1
shortCondition = direction1 == 1 and direction2 == 1 and direction1 == -1
// Optional: Plot Supertrends
plot(supertrend1, "Supertrend 1", color = direction1 == -1 ? color.green : color.red, linewidth=3)
plot(supertrend2, "Supertrend 2", color = direction2 == -1 ? color.lime : color.maroon, linewidth=3)
plotshape(series=longCondition, location=location.belowbar, color=color.green, style=shape.triangleup, title="Long")
plotshape(series=shortCondition, location=location.abovebar, color=color.red, style=shape.triangledown, title="Short")
signal = longCondition ? 1 : shortCondition ? -1 : na
plot(signal, "Signal", display = display.data_window)
To connect your indicator to this FTMO rules monitor template, please update it as follow
Create a signal variable to store 1 for the long/buy signal or -1 for the short/sell signal
Plot it in the display.data_window panel so that it doesn't clutter your chart
signal = longCondition ? 1 : shortCondition ? -1 : na
plot(signal, "Signal", display = display.data_window)
In the FTMO Rules Monitor template, I'm capturing this external signal with this input.source variable
entry_connector = input.source(close, "Entry Connector", group="Entry Connector")
longCondition = entry_connector == 1
shortCondition = entry_connector == -1
🔶 USAGE
This indicator displays essential FTMO Challenge rules and tracks your progress toward meeting each one. Here’s what’s monitored:
Max Daily Loss
• 10k Account: $500
• 25k Account: $1,250
• 50k Account: $2,500
• 100k Account: $5,000
• 200k Account: $10,000
Max Total Loss
• 10k Account: $1,000
• 25k Account: $2,500
• 50k Account: $5,000
• 100k Account: $10,000
• 200k Account: $20,000
Profit Target
• 10k Account: $1,000
• 25k Account: $2,500
• 50k Account: $5,000
• 100k Account: $10,000
• 200k Account: $20,000
Minimum Trading Days: 4 consecutive days for all account sizes
🔹 Key Features
1. Real-Time Compliance Check
The FTMO Rules Monitor keeps track of your daily and total losses, profit targets, and trading days. Each metric updates in real-time, giving you peace of mind that you’re within FTMO’s rules.
2. Color-Coded Visual Feedback
Each rule’s status is shown clearly with a ✓ for compliance or ✗ if the limit is breached. When a rule is broken, the indicator highlights it in red, so there’s no confusion.
3. Completion Notification
Once all FTMO requirements are met, the indicator closes all open positions and displays a celebratory message on your chart, letting you know you’ve successfully completed the challenge.
4. Easy-to-Read Table
A table on your chart provides an overview of each rule, your target, current performance, and whether you’re meeting each goal. The table adjusts its color scheme based on your chart settings for optimal visibility.
5. Dynamic Position Sizing
Integrated ATR-based position sizing helps you manage risk and avoid large drawdowns, ensuring each trade aligns with FTMO’s risk management principles.
Daveatt
TrendGuard Scalper: SSL + Hama Candle with Consolidation ZonesThis TradingView script brings a powerful scalping strategy that combines the SSL Channel and Hama Candles indicators with a special twist—consolidation detection. Designed for traders looking for consistency in various markets like crypto, forex, and stocks, this strategy highlights clear trend signals, risk management, and helps filter out risky trades during consolidation periods.
Why Use This Strategy?
Clear Trend Detection:
With the SSL Channel, you’ll know exactly when the market is in an uptrend (green) or downtrend (red), giving you straightforward entry points.
Short-Term Trend Precision with Hama Candles:
By calculating unique EMAs for open, high, low, and close, the Hama Candles show the strength and direction of short-term trends. Combined with the Hama Line, it gives you a solid confirmation on whether the trend is strong or about to reverse, allowing for precise entries and exits.
Avoiding Choppy Markets:
Thanks to ATR-based consolidation detection, this strategy identifies low-volatility periods where the market is “choppy” and less predictable. During these times, a yellow background appears on the chart, warning you to hold off on trades, reducing the likelihood of entering losing trades.
Built-In Risk Management:
With adjustable Take Profit and Stop Loss levels based on price movements, you can set and forget your trades, with a safety net if the market turns against you. The strategy automatically closes positions if the price returns to the Hama Candle, keeping your risk low.
How It Works:
Long Position: When both the SSL and Hama indicators show a green trend, and the price is above the Hama Candles, the strategy opens a long position. Take Profit triggers at your chosen risk-to-reward ratio, while Stop Loss protects you just below the Hama Line.
Short Position: When both indicators align in red and the price is below the Hama Candles, the strategy opens a short. Similar to longs, Stop Loss is set just above the Hama Line, and Take Profit is at your defined level.
Start Trading Confidently
Test this strategy with different settings and discover how it can perform across various assets. Whether you're trading Bitcoin, forex pairs, or stocks, this system has the flexibility and robustness to help you spot profitable trends and avoid risky zones. Try it today on a 30-minute timeframe to see how it aligns with your trading goals, and let the consolidation detection guide you away from false signals.
Happy trading, and may the trends be with you! 📈
Martingale with MACD+KDJ opening conditionsStrategy Overview:
This strategy is based on a Martingale trading approach, incorporating MACD and KDJ indicators. It features pyramiding, trailing stops, and dynamic profit-taking mechanisms, suitable for both long and short trades. The strategy increases position size progressively using a Multiplier, a key feature of Martingale systems.
Key Concepts:
Martingale Strategy: A trading system where positions are doubled or increased after a loss to recover previous losses with a single successful trade. In this script, the position size is incremented using a Multiplier for each addition.
Pyramiding: Allows adding to existing trades when market conditions are favorable, enhancing profitability during trends.
Settings:
Basic Inputs:
Initial Order: Defines the starting size of the position.
Default: 150.0
MACD Settings: Customize the fast, slow, and signal smoothing lengths.
Default: Fast Length: 9, Slow Length: 26, Signal Smoothing: 9
KDJ Settings: Customize the length and smoothing parameters for KDJ.
Default: Length: 14, Smooth K: 3, Smooth D: 3
Max Additions: Sets the number of additional positions (pyramiding).
Default: 5 (Min: 1, Max: 10)
Position Sizing: Percent to add to positions on favorable conditions.
Default: 1.0%
Martingale Multiplier:
Add Multiplier: This value controls the scaling of additional positions according to the Martingale principle. After each loss, a new position is added, and its size is increased by the Multiplier factor. For example, with a multiplier of 2, each new addition will be twice as large as the previous one, accelerating recovery if the price moves favorably.
Default: 1.0 (no multiplication)
Can be adjusted up to 10x to aggressively increase position size after losses.
Trade Execution:
Long Trades:
Entry Condition: A long position is opened when the MACD line crosses over the signal line, and the KDJ’s %K crosses above %D.
Additions (Martingale): After the initial long position, new positions are added if the price drops by the defined percentage, and each new addition is increased using the Multiplier. This continues up to the set Max Additions.
Short Trades:
Entry Condition: A short position is opened when the MACD line crosses under the signal line, and the KDJ’s %K crosses below %D.
Additions (Martingale): After the initial short position, new positions are added if the price rises by the defined percentage, and each new addition is increased using the Multiplier.
Exit Conditions:
Take Profit: Exits are triggered when the price reaches the take-profit threshold.
Stop Loss: If the price moves unfavorably, the position will be closed at the set stop-loss level.
Trailing Stop: Adjusts dynamically as the price moves in favor of the trade to lock in profits.
On-Chart Visuals:
Long Signals: Blue triangles below the bars indicate long entries, and green triangles mark additional long positions.
Short Signals: Red triangles above the bars indicate short entries, and orange triangles mark additional short positions.
Information Table:
The strategy displays a table with key metrics:
Open Price: The entry price of the trade.
Average Price: The average price of the current position.
Additions: The number of additional positions taken.
Next Add Price: The price level for the next position.
Take Profit: The price at which profits will be taken.
Stop Loss: The stop-loss level to minimize risk.
Usage Instructions:
Adjust the parameters to your trading style using the input settings.
The Multiplier amplifies your position size after each addition, so use it cautiously, especially in volatile markets.
Monitor the signals and table on the chart for entry/exit decisions and trade management.
RSI Crossover Strategy with Compounding (Monthly)Explanation of the Code:
Initial Setup:
The strategy initializes with a capital of 100,000.
Variables track the capital and the amount invested in the current trade.
RSI Calculation:
The RSI and its SMA are calculated on the monthly timeframe using request.security().
Entry and Exit Conditions:
Entry: A long position is initiated when the RSI is above its SMA and there’s no existing position. The quantity is based on available capital.
Exit: The position is closed when the RSI falls below its SMA. The capital is updated based on the net profit from the trade.
Capital Management:
After closing a trade, the capital is updated with the net profit plus the initial investment.
Plotting:
The RSI and its SMA are plotted for visualization on the chart.
A label displays the current capital.
Notes:
Test the strategy on different instruments and historical data to see how it performs.
Adjust parameters as needed for your specific trading preferences.
This script is a basic framework, and you might want to enhance it with risk management, stop-loss, or take-profit features as per your trading strategy.
Feel free to modify it further based on your needs!
ADX + Volume Strategy### Strategy Description: ADX and Volume-Based Trading Strategy
This strategy is designed to identify strong market trends using the **Average Directional Index (ADX)** and confirm trading signals with **Volume**. The idea behind the strategy is to enter trades only when the market shows a strong trend (as indicated by ADX) and when the price movement is supported by high trading volume. This combination helps filter out weaker signals and provides more reliable entries into positions.
### Key Indicators:
1. **ADX (Average Directional Index)**:
- **Purpose**: ADX is a technical indicator that measures the strength of a trend, regardless of its direction (up or down).
- **Usage**: The strategy uses ADX to determine whether the market is trending strongly. If ADX is above a certain threshold (default is 25), it indicates that a strong trend is present.
- **Directional Indicators**:
- **DI+ (Directional Indicator Plus)**: Indicates the strength of the upward price movement.
- **DI- (Directional Indicator Minus)**: Indicates the strength of the downward price movement.
- ADX does not indicate the direction of the trend but confirms that a trend exists. DI+ and DI- are used to determine the direction.
2. **Volume**:
- **Purpose**: Volume is a key indicator for confirming the strength of a price movement. High volume suggests that a large number of market participants are supporting the movement, making it more likely to continue.
- **Usage**: The strategy compares the current volume to the 20-period moving average of the volume. The trade signal is confirmed if the current volume is greater than the average volume by a specified **Volume Multiplier** (default multiplier is 1.5). This ensures that the trade is supported by strong market participation.
### Strategy Logic:
#### **Entry Conditions:**
1. **Long Position** (Buy):
- **ADX** is above the threshold (default is 25), indicating a strong trend.
- **DI+ > DI-**, signaling that the market is trending upward.
- The **current volume** is greater than the 20-period average volume multiplied by the **Volume Multiplier** (e.g., 1.5), indicating that the upward price movement is backed by sufficient market activity.
2. **Short Position** (Sell):
- **ADX** is above the threshold (default is 25), indicating a strong trend.
- **DI- > DI+**, signaling that the market is trending downward.
- The **current volume** is greater than the 20-period average volume multiplied by the **Volume Multiplier** (e.g., 1.5), indicating that the downward price movement is backed by strong selling activity.
#### **Exit Conditions**:
- Positions are closed when the opposite signal appears:
- **For long positions**: Close when the short conditions are met (ADX still above the threshold, DI- > DI+, and the volume condition holds).
- **For short positions**: Close when the long conditions are met (ADX still above the threshold, DI+ > DI-, and the volume condition holds).
### Parameters:
- **ADX Period**: The period used to calculate ADX (default is 14). This controls how sensitive the ADX is to price movements.
- **ADX Threshold**: The minimum ADX value required for the strategy to consider the market trend as strong (default is 25). Higher values focus on stronger trends.
- **Volume Multiplier**: This parameter adjusts how much higher the current volume needs to be compared to the 20-period moving average for the signal to be valid. A value of 1.5 means the current volume must be 50% higher than the average volume.
### Example Trade Flow:
1. **Long Trade Example**:
- ADX > 25, confirming a strong trend.
- DI+ > DI-, confirming that the trend direction is upward.
- The current volume is 50% higher than the 20-period average volume (multiplied by 1.5).
- **Action**: Enter a long position.
2. **Short Trade Example**:
- ADX > 25, confirming a strong trend.
- DI- > DI+, confirming that the trend direction is downward.
- The current volume is 50% higher than the 20-period average volume.
- **Action**: Enter a short position.
### Strengths of the Strategy:
- **Trend Filtering**: The strategy ensures that trades are only taken when the market is trending strongly (confirmed by ADX) and that the price movement is supported by high volume, reducing the likelihood of false signals.
- **Volume Confirmation**: Using volume as confirmation provides an additional layer of reliability, as volume spikes often accompany sustained price moves.
- **Dual Signal Confirmation**: Both trend strength (ADX) and volume conditions must be met for a trade, making the strategy more robust.
### Weaknesses of the Strategy:
- **Limited Effectiveness in Range-Bound Markets**: Since the strategy relies on strong trends, it may underperform in sideways or non-trending markets where ADX stays below the threshold.
- **Lagging Nature of ADX**: ADX is a lagging indicator, which means that it may confirm the trend after it has already begun, potentially leading to late entries.
- **Volume Requirement**: In low-volume markets, the volume multiplier condition may not be met often, leading to fewer trade opportunities.
### Customization:
- **Adjust the ADX Threshold**: You can raise the threshold if you want to focus only on very strong trends, or lower it to capture moderate trends.
- **Adjust the Volume Multiplier**: You can change the multiplier to be more or less strict. A higher multiplier (e.g., 2.0) will require a stronger volume spike to confirm the signal, while a lower multiplier (e.g., 1.2) will allow more trades with weaker volume confirmation.
### Summary:
This ADX and Volume strategy is ideal for traders who want to follow strong trends while ensuring that the trend is supported by high trading volume. By combining a trend strength filter (ADX) and volume confirmation, the strategy aims to increase the probability of entering profitable trades while reducing the number of false signals. However, it may underperform in range-bound markets or in markets with low volume.
High/Low Breakout Statistical Analysis StrategyThis Pine Script strategy is designed to assist in the statistical analysis of breakout systems on a monthly, weekly, or daily timeframe. It allows the user to select whether to open a long or short position when the price breaks above or below the respective high or low for the chosen timeframe. The user can also define the holding period for each position in terms of bars.
Core Functionality:
Breakout Logic:
The strategy triggers trades based on price crossing over (for long positions) or crossing under (for short positions) the high or low of the selected period (daily, weekly, or monthly).
Timeframe Selection:
A dropdown menu enables the user to switch between the desired timeframe (monthly, weekly, or daily).
Trade Direction:
Another dropdown allows the user to select the type of trade (long or short) depending on whether the breakout occurs at the high or low of the timeframe.
Holding Period:
Once a trade is opened, it is automatically closed after a user-defined number of bars, making it useful for analyzing how breakout signals perform over short-term periods.
This strategy is intended exclusively for research and statistical purposes rather than real-time trading, helping users to assess the behavior of breakouts over different timeframes.
Relevance of Breakout Systems:
Breakout trading systems, where trades are executed when the price moves beyond a significant price level such as the high or low of a given period, have been extensively studied in financial literature for their potential predictive power.
Momentum and Trend Following:
Breakout strategies are a form of momentum-based trading, exploiting the tendency of prices to continue moving in the direction of a strong initial movement after breaching a critical support or resistance level. According to academic research, momentum strategies, including breakouts, can produce returns above average market returns when applied consistently. For example, Jegadeesh and Titman (1993) demonstrated that stocks that performed well in the past 3-12 months continued to outperform in the subsequent months, suggesting that price continuation patterns, like breakouts, hold value .
Market Efficiency Hypothesis:
While the Efficient Market Hypothesis (EMH) posits that markets are generally efficient, and it is difficult to outperform the market through technical strategies, some studies show that in less liquid markets or during specific times of market stress, breakout systems can capitalize on temporary inefficiencies. Taylor (2005) and other researchers have found instances where breakout systems can outperform the market under certain conditions.
Volatility and Breakouts:
Breakouts are often linked to periods of increased volatility, which can generate trading opportunities. Coval and Shumway (2001) found that periods of heightened volatility can make breakouts more significant, increasing the likelihood that price trends will follow the breakout direction. This correlation between volatility and breakout reliability makes it essential to study breakouts across different timeframes to assess their potential profitability .
In summary, this breakout strategy offers an empirical way to study price behavior around key support and resistance levels. It is useful for researchers and traders aiming to statistically evaluate the effectiveness and consistency of breakout signals across different timeframes, contributing to broader research on momentum and market behavior.
References:
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Fama, E. F., & French, K. R. (1996). Multifactor Explanations of Asset Pricing Anomalies. Journal of Finance, 51(1), 55-84.
Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
Coval, J. D., & Shumway, T. (2001). Expected Option Returns. Journal of Finance, 56(3), 983-1009.
Simplified Gap Strategy with SMA FilterThe Simplified Gap Strategy leverages price gaps as a trading signal, focusing on their significance in market behavior. Gaps occur when the opening price of a security differs significantly from the previous closing price, often signaling potential continuation or reversal patterns.
Key Features:
Gap Threshold:
This strategy requires a minimum percentage gap (defined by the user) to qualify for trading signals.
Directional Trading:
Users can select from various gap types, including "Long Up Gap" and "Short Down Gap," allowing for tailored trading approaches.
SMA Filter:
An optional Simple Moving Average (SMA) filter helps refine trade entries based on trend direction, increasing the probability of successful trades.
Hold Duration:
Positions can be held for a user-defined duration, providing flexibility in trade management.
Statistical Significance of Gaps:
Research has shown that gaps can provide insights into future price movements. According to studies such as those by Hutton and Jiang (2008), price gaps are often followed by momentum in the direction of the gap, indicating that they can serve as reliable indicators for traders. The "Gap Theory" suggests that gaps are filled approximately 90% of the time, emphasizing their relevance in market dynamics (Nikkinen, Sahlström, & Kinnunen, 2006).
Important Note:
This strategy is designed solely for statistical analysis and should not be construed as financial advice. Users are encouraged to conduct their own research and analysis before applying this strategy in live trading scenarios.
By understanding the underlying mechanisms of price gaps and their statistical significance, traders can enhance their decision-making processes and potentially improve trading outcomes.
References:
Hutton, A. W., & Jiang, W. (2008). "Price Gaps: A Guide to Trading Gaps."
Nikkinen, J., Sahlström, P., & Kinnunen, J. (2006). "The Gaps in Financial Markets: An Empirical Study."
This description provides an overview of the strategy while emphasizing its analytical purpose and backing it with relevant academic insights.
Streak-Based Trading StrategyThe strategy outlined in the provided script is a streak-based trading strategy that focuses on analyzing winning and losing streaks. It’s important to emphasize that this strategy is not intended for actual trading but rather for statistical analysis of streak series.
How the Strategy Works
1. Parameter Definition:
• Trade Direction: Users can choose between “Long” (buy) and “Short” (sell).
• Streak Threshold: Defines how many consecutive wins or losses are needed to trigger a trade.
• Hold Duration: Specifies how many periods the position will be held.
• Doji Threshold: Determines the sensitivity for Doji candles, which indicate market uncertainty.
2. Streak Calculation:
• The script identifies Doji candles and counts winning and losing streaks based on the closing price compared to the previous closing price.
• Streak counting occurs only when no position is currently held.
3. Trade Conditions:
• If the loss streak reaches the defined threshold and the trade direction is “Long,” a buy position is opened.
• If the win streak is met and the trade direction is “Short,” a sell position is opened.
• The position is held for the specified duration.
4. Visualization:
• Winning and losing streaks are plotted as histograms to facilitate analysis.
Scientific Basis
The concept of analyzing streaks in financial markets is well-documented in behavioral economics and finance. Studies have shown that markets often exhibit momentum and trend-following behavior, meaning the likelihood of consecutive winning or losing periods can be higher than what random statistics would suggest (see, for example, “The Behavior of Stock-Market Prices” by Eugene Fama).
Additionally, empirical research indicates that investors often make decisions based on psychological factors influenced by streaks. This can lead to irrational behavior, as they may focus on past wins or losses (see “Behavioral Finance: Psychology, Decision-Making, and Markets” by R. M. F. F. Thaler).
Overall, this strategy serves as a tool for statistical analysis of streak series, providing deeper insights into market behavior and trends rather than being directly used for trading decisions.
GC Strategy with Trend Filter and Sudden Move Profit TakingBYBIT:BTCUSDT.P 15M
Situation Assessment with Three Moving Averages
The strategy uses the crossover of the 5SMA and 25SMA as entry signals.
Additionally, the 75SMA is used as a filter. Long entries are only allowed when the price is above the 75SMA, and short entries are only allowed when the price is below the 75SMA.
ADX Filter
The Average Directional Index (ADX) is used to check the strength of the trend. Entry signals are only activated when the ADX is above 20. This ensures that trades are only executed when the trend is strong.
Sudden Move Detection
The strategy detects sudden price movements. If a sudden move occurs, the position is closed to lock in profits.
Entry
Long Entry: When the 5SMA crosses above the 25SMA, the price is above the 75SMA, and the ADX is above 20.
Short Entry: When the 5SMA crosses below the 25SMA, the price is below the 75SMA, and the ADX is above 20.
Exit
Positions are closed if a sudden move occurs. Positions are also closed if an opposing entry signal is generated.
This strategy aims to confirm the strength of the trend using moving average crossovers and ADX and to lock in profits based on sudden price movements.
３本の移動平均線による状況判断
5SMAと25SMA のクロスオーバーをエントリーシグナルとして使用します。
さらに、75SMAをフィルターとして使用し、価格が75SMAの上にある場合のみロングエントリーを許可し、75SMAの下にある場合のみショートエントリーを許可します。
ADXフィルター
ADX（平均方向性指数）を使ってトレンドの強さを確認します。
ADXが20より大きい場合のみ、エントリーシグナルを有効にします。これにより、トレンドが強い時にのみ取引を行うことができます。
急激な変動検知
価格の急激な変動を検出します。
急激な変動があった場合には、ポジションをクローズして利益を確定します。
エントリー
ロングエントリー
5SMAが25SMAを上にクロスし、価格が75SMAの上にあり、ADXが20を超えているとき。
ショートエントリー
5SMAが25SMAを下にクロスし、価格が75SMAの下にあり、ADXが20を超えているとき。
エグジット
急激な変動があった場合、ポジションをクローズします。
反対のエントリーシグナルが発生した場合にも、ポジションをクローズします。
このストラテジーは、移動平均のクロスオーバーとADXを使ってトレンドの強さを確認し、急激な変動に基づいて利益を確定することを目的としています。
Fibonacci-Only StrategyFibonacci-Only Strategy
This script is a custom trading strategy designed for traders who leverage Fibonacci retracement levels to identify potential trade entries and exits. The strategy is versatile, allowing users to trade across multiple timeframes, with built-in options for dynamic stop loss, trailing stops, and take profit levels.
Key Features:
Custom Fibonacci Levels:
This strategy calculates three specific Fibonacci retracement levels: 19%, 82.56%, and the reverse 19% level. These levels are used to identify potential areas of support and resistance where price reversals or breaks might occur.
The Fibonacci levels are calculated based on the highest and lowest prices within a 100-bar period, making them dynamic and responsive to recent market conditions.
Dynamic Entry Conditions:
Touch Entry: The script enters long or short positions when the price touches specific Fibonacci levels and confirms the move with a bullish (for long) or bearish (for short) candle.
Break Entry (Optional): If the "Use Break Strategy" option is enabled, the script can also enter positions when the price breaks through Fibonacci levels, providing more aggressive entry opportunities.
Stop Loss Management:
The script offers flexible stop loss settings. Users can choose between a fixed percentage stop loss or an ATR-based stop loss, which adjusts based on market volatility.
The ATR (Average True Range) stop loss is multiplied by a user-defined factor, allowing for tailored risk management based on market conditions.
Trailing Stop Mechanism:
The script includes an optional trailing stop feature, which adjusts the stop loss level as the market moves in favor of the trade. This helps lock in profits while allowing the trade to run if the trend continues.
The trailing stop is calculated as a percentage of the difference between the entry price and the current market price.
Multiple Take Profit Levels:
The strategy calculates seven take profit levels, each at incremental percentages above (for long trades) or below (for short trades) the entry price. This allows for gradual profit-taking as the market moves in the trade's favor.
Each take profit level can be customized in terms of the percentage of the position to be closed, providing precise control over exit strategies.
Strategy Backtesting and Results:
Realistic Backtesting:
The script has been backtested with realistic account sizes, commission rates, and slippage settings to ensure that the results are applicable to actual trading scenarios.
The backtesting covers various timeframes and markets to ensure the strategy's robustness across different trading environments.
Default Settings:
The script is published with default settings that have been optimized for general use. These settings include a 15-minute timeframe, a 1.0% stop loss, a 2.0 ATR multiplier for stop loss, and a 1.5% trailing stop.
Users can adjust these settings to better fit their specific trading style or the market they are trading.
How It Works:
Long Entry Conditions:
The strategy enters a long position when the price touches the 19% Fibonacci level (from high to low) or the reverse 19% level (from low to high) and confirms the move with a bullish candle.
If the "Use Break Strategy" option is enabled, the script will also enter a long position when the price breaks below the 19% Fibonacci level and then moves back up, confirming the break with a bullish candle.
Short Entry Conditions:
The strategy enters a short position when the price touches the 82.56% Fibonacci level and confirms the move with a bearish candle.
If the "Use Break Strategy" option is enabled, the script will also enter a short position when the price breaks above the 82.56% Fibonacci level and then moves back down, confirming the break with a bearish candle.
Stop Loss and Take Profit Logic:
The stop loss for each trade is calculated based on the selected method (fixed percentage or ATR-based). The strategy then manages the trade by either trailing the stop or taking profit at predefined levels.
The take profit levels are set at increments of 0.5% above or below the entry price, depending on whether the position is long or short. The script gradually exits the trade as these levels are hit, securing profits while minimizing risk.
Usage:
For Fibonacci Traders:
This script is ideal for traders who rely on Fibonacci retracement levels to find potential trade entries and exits. The script automates the process, allowing traders to focus on market analysis and decision-making.
For Trend and Swing Traders:
The strategy's flexibility in handling both touch and break entries makes it suitable for trend-following and swing trading strategies. The multiple take profit levels allow traders to capture profits in trending markets while managing risk.
Important Notes:
Originality: This script uniquely combines Fibonacci retracement levels with dynamic stop loss management and multiple take profit levels. It is not just a combination of existing indicators but a thoughtful integration designed to enhance trading performance.
Disclaimer: Trading involves risk, and it is crucial to test this script in a demo account or through backtesting before applying it to live trading. Users should ensure that the settings align with their individual risk tolerance and trading strategy.
Strategic Multi-Step Supertrend - Strategy [presentTrading]The code is mainly developed for me to stimulate the multi-step taking profit function for strategies. The result shows the drawdown can be reduced but at the same time reduced the profit as well. It can be a heuristic for futures leverage traders.
█ Introduction and How it is Different
The "Strategic Multi-Step Supertrend" is a trading strategy designed to leverage the power of multiple steps to optimize trade entries and exits across the Supertrend indicator. Unlike traditional strategies that rely on single entry and exit points, this strategy employs a multi-step approach to take profit, allowing traders to lock in gains incrementally. Additionally, the strategy is adaptable to both long and short trades, providing a comprehensive solution for dynamic market conditions.
This template strategy lies in its dual Supertrend calculation, which enhances the accuracy of trend detection and provides more reliable signals for trade entries and exits. This approach minimizes false signals and increases the overall profitability of trades by ensuring that positions are entered and exited at optimal points.
BTC 6h L/S Performance
█ Strategy, How It Works: Detailed Explanation
The "Strategic Multi-Step Supertrend Trader" strategy utilizes two Supertrend indicators calculated with different parameters to determine the direction and strength of the market trend. This dual approach increases the robustness of the signals, reducing the likelihood of entering trades based on false signals. Here is a detailed breakdown of how the strategy operates:
🔶 Supertrend Indicator Calculation
The Supertrend indicator is a trend-following overlay on the price chart, typically used to identify the direction of the trend. It is calculated using the Average True Range (ATR) to ensure that the indicator adapts to market volatility. The formula for the Supertrend indicator is:
Upper Band = (High + Low) / 2 + (Factor * ATR)
Lower Band = (High + Low) / 2 - (Factor * ATR)
Where:
- High and Low are the highest and lowest prices of the period.
- Factor is a user-defined multiplier.
- ATR is the Average True Range over a specified period.
The Supertrend changes its direction based on the closing price in relation to these bands.
🔶 Entry-Exit Conditions
The strategy enters long positions when both Supertrend indicators signal an uptrend, and short positions when both indicate a downtrend. Specifically:
- Long Condition: Supertrend1 < 0 and Supertrend2 < 0
- Short Condition: Supertrend1 > 0 and Supertrend2 > 0
- Long Exit Condition: Supertrend1 > 0 and Supertrend2 > 0
- Short Exit Condition: Supertrend1 < 0 and Supertrend2 < 0
🔶 Multi-Step Take Profit Mechanism
The strategy features a multi-step take profit mechanism, which allows traders to lock in profits incrementally. This is achieved through four user-configurable take profit levels. For each level, the strategy specifies a percentage increase (for long trades) or decrease (for short trades) in the entry price at which a portion of the position is exited:
- Step 1: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent1 / 100)
- Step 2: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent2 / 100)
- Step 3: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent3 / 100)
- Step 4: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent4 / 100)
This staggered exit strategy helps in locking profits at multiple levels, thereby reducing risk and increasing the likelihood of capturing the maximum possible profit from a trend.
BTC Local
█ Trade Direction
The strategy is highly flexible, allowing users to specify the trade direction. There are three options available:
- Long Only: The strategy will only enter long trades.
- Short Only: The strategy will only enter short trades.
- Both: The strategy will enter both long and short trades based on the Supertrend signals.
This flexibility allows traders to adapt the strategy to various market conditions and their own trading preferences.
█ Usage
1. Add the strategy to your trading platform and apply it to the desired chart.
2. Configure the take profit settings under the "Take Profit Settings" group.
3. Set the trade direction under the "Trade Direction" group.
4. Adjust the Supertrend settings in the "Supertrend Settings" group to fine-tune the indicator calculations.
5. Monitor the chart for entry and exit signals as indicated by the strategy.
█ Default Settings
- Use Take Profit: True
- Take Profit Percentages: Step 1 - 6%, Step 2 - 12%, Step 3 - 18%, Step 4 - 50%
- Take Profit Amounts: Step 1 - 12%, Step 2 - 8%, Step 3 - 4%, Step 4 - 0%
- Number of Take Profit Steps: 3
- Trade Direction: Both
- Supertrend Settings: ATR Length 1 - 10, Factor 1 - 3.0, ATR Length 2 - 11, Factor 2 - 4.0
These settings provide a balanced starting point, which can be customized further based on individual trading preferences and market conditions.
Adaptive RSI StrategyThe Adaptive RSI Strategy is designed to give you an edge by adapting to changing market conditions more effectively than the traditional RSI. By adjusting dynamically to recent price movements, this strategy aims to provide more timely and accurate trade signals.
How Does It Work?
You can set the number of periods for the RSI calculation. The default is 14, but feel free to experiment with different lengths to suit your trading style.
Choose the price data to base the RSI on, typically the closing price.
Decide if you want the strategy to visually highlight upward and downward movements of the Adaptive RSI (ARSI) on the chart. This can help you quickly spot trends.
Adaptive Calculation:
Alpha: The strategy uses an adaptive factor called alpha, which changes based on recent RSI values. This makes the RSI more sensitive to recent market conditions.
Adaptive RSI (ARSI): This is the core of our strategy. It calculates the ARSI using the adaptive alpha, making it more responsive to price changes compared to the traditional RSI.
Trade Signals:
Long Entry (Buy Signal): The strategy triggers a buy signal when the ARSI value crosses above its previous value. This indicates a potential upward trend, suggesting it's a good time to enter a long position.
Short Entry (Sell Signal): Conversely, a sell signal is triggered when the ARSI value crosses below its previous value, indicating a potential downward trend and suggesting it's a good time to enter a short position.
Visual Representation:
If you enable the highlight movements feature, the ARSI line on the chart will change color: green for upward movements and red for downward movements. This makes it easier to see potential trade opportunities at a glance.
Why Use the Adaptive RSI Strategy?
Responsiveness: The adaptive nature of this strategy means it's more sensitive to market changes, helping you react quicker to new trends.
Customization: You can tailor the length of the RSI period and decide whether to highlight movements, allowing you to adapt the strategy to your specific needs and preferences.
Visual Clarity: Highlighting the ARSI movements on the chart makes it easier to spot trends and potential entry points, giving you a clearer picture of the market.
Trailing Take Profit - Close Based📝 Description
This script demonstrates a new approach to the trailing take profit.
Trailing Take Profit is a price-following technique. When used, instead of setting a limit order for the take profit target exiting from your position at the specified price, a stop order is conditionally set when the take profit target is reached. Then, the stop price (a.k.a trailing price), is placed below the take profit target at a distance defined by the user percentagewise. On regular time intervals, the stop price gets updated by following the "Trail Barrier" price (high by default) upwards. When the current price hits the stop price you exit the trade. Check the chart for more details.
This script demonstrates how to implement the close-based Trailing Take Profit logic for long positions, but it can also be applied for short positions if the logic is "reversed".
📢 NOTE
To generate some entries and showcase the "Trailing Take Profit" technique, this script uses the crossing of two moving averages. Please keep in mind that you should not relate the Backtesting results you see in the "Strategy Tester" tab with the success of the technique itself.
This is not a complete strategy per se, and the backtest results are affected by many parameters that are outside of the scope of this publication. If you choose to use this new approach of the "Trailing Take Profit" in your logic you have to make sure that you are backtesting the whole strategy.
⚔️ Comparison
In contrast to my older "Trailing Take Profit" publication where the trailing take profit implementation was tick-based, this new approach is close-based, meaning that the update of the stop price occurs at the bar close instead of every tick.
While comparing the real-time results of the two implementations is like comparing apples to oranges, because they have different dynamic behavior, the new approach offers better consistency between the backtesting results and the real-time results.
By updating the stop price on every bar close, you do not rely on the backtester assumptions anymore (check the Reasoning section below for more info).
The new approach resembles the conditional "Trailing Exit" technique, where the condition is true when the current price crosses over the take profit target. Then, the stop order is placed at the trailing price and it gets updated on every bar close to "follow" the barrier price (high). On the other hand, the older tick-based approach had more "tight" dynamics since the trailing price gets updated on every tick leaving less room for price fluctuations by making it more probable to reach the trailing price.
🤔 Reasoning
This new close-based approach addresses several practical issues the older tick-based approach had. Those issues arise mainly from the technicalities of the TV Backtester. More specifically, due to the assumptions the Broker Emulator makes for the price action of the history bars, the backtesting results in the TV Backtester are exaggerated, and depending on the timeframe, the backtesting results look way better than they are in reality.
The effect above, and the inability to reason about the performance of a strategy separated people into two groups. Those who never use this feature, because they couldn't know for sure the actual effect it might have in their strategy, (even if it turned out to be more profitable) and those who abused this type of "repainting" behavior to show off, and hijack some boosts from the community by boasting about the "fake" results of their strategies.
Even if there are ways to evaluate the effectiveness of the tick-based approach that is applied in an existing strategy (this is out of the topic of this publication), it requires extra effort to do the analysis. Using this closed-based approach we can have more predictable results, without surprises.
⚠️ Caveats
Since this approach updates the trailing price on bar close, you must wait for at least one bar to close after the price crosses over the take profit target.
Trend Deviation strategy - BTC [IkkeOmar]Intro:
This is an example if anyone needs a push to get started with making strategies in pine script. This is an example on BTC, obviously it isn't a good strategy, and I wouldn't share my own good strategies because of alpha decay.
This strategy integrates several technical indicators to determine market trends and potential trade setups. These indicators include:
Directional Movement Index (DMI)
Bollinger Bands (BB)
Schaff Trend Cycle (STC)
Moving Average Convergence Divergence (MACD)
Momentum Indicator
Aroon Indicator
Supertrend Indicator
Relative Strength Index (RSI)
Exponential Moving Average (EMA)
Volume Weighted Average Price (VWAP)
It's crucial for you guys to understand the strengths and weaknesses of each indicator and identify synergies between them to improve the strategy's effectiveness.
Indicator Settings:
DMI (Directional Movement Index):
Length: This parameter determines the number of bars used in calculating the DMI. A higher length may provide smoother results but might lag behind the actual price action.
Bollinger Bands:
Length: This parameter specifies the number of bars used to calculate the moving average for the Bollinger Bands. A longer length results in a smoother average but might lag behind the price action.
Multiplier: The multiplier determines the width of the Bollinger Bands. It scales the standard deviation of the price data. A higher multiplier leads to wider bands, indicating increased volatility, while a lower multiplier results in narrower bands, suggesting decreased volatility.
Schaff Trend Cycle (STC):
Length: This parameter defines the length of the STC calculation. A longer length may result in smoother but slower-moving signals.
Fast Length: Specifies the length of the fast moving average component in the STC calculation.
Slow Length: Specifies the length of the slow moving average component in the STC calculation.
MACD (Moving Average Convergence Divergence):
Fast Length: Determines the number of bars used to calculate the fast EMA (Exponential Moving Average) in the MACD.
Slow Length: Specifies the number of bars used to calculate the slow EMA in the MACD.
Signal Length: Defines the number of bars used to calculate the signal line, which is typically an EMA of the MACD line.
Momentum Indicator:
Length: This parameter sets the number of bars over which momentum is calculated. A longer length may provide smoother momentum readings but might lag behind significant price changes.
Aroon Indicator:
Length: Specifies the number of bars over which the Aroon indicator calculates its values. A longer length may result in smoother Aroon readings but might lag behind significant market movements.
Supertrend Indicator:
Trendline Length: Determines the length of the period used in the Supertrend calculation. A longer length results in a smoother trendline but might lag behind recent price changes.
Trendline Factor: Specifies the multiplier used in calculating the trendline. It affects the sensitivity of the indicator to price changes.
RSI (Relative Strength Index):
Length: This parameter sets the number of bars over which RSI calculates its values. A longer length may result in smoother RSI readings but might lag behind significant price changes.
EMA (Exponential Moving Average):
Fast EMA: Specifies the number of bars used to calculate the fast EMA. A shorter period results in a more responsive EMA to recent price changes.
Slow EMA: Determines the number of bars used to calculate the slow EMA. A longer period results in a smoother EMA but might lag behind recent price changes.
VWAP (Volume Weighted Average Price):
Default settings are typically used for VWAP calculations, which consider the volume traded at each price level over a specific period. This indicator provides insights into the average price weighted by trading volume.
backtest range and rules:
You can specify the start date for backtesting purposes.
You can can select the desired trade direction: Long, Short, or Both.
Entry and Exit Conditions:
LONG:
DMI Cross Up: The Directional Movement Index (DMI) indicates a bullish trend when the positive directional movement (+DI) crosses above the negative directional movement (-DI).
Bollinger Bands (BB): The price is below the upper Bollinger Band, indicating a potential reversal from the upper band.
Momentum Indicator: Momentum is positive, suggesting increasing buying pressure.
MACD (Moving Average Convergence Divergence): The MACD line is above the signal line, indicating bullish momentum.
Supertrend Indicator: The Supertrend indicator signals an uptrend.
Schaff Trend Cycle (STC): The STC indicates a bullish trend.
Aroon Indicator: The Aroon indicator signals a bullish trend or crossover.
When all these conditions are met simultaneously, the strategy considers it a favorable opportunity to enter a long trade.
SHORT:
DMI Cross Down: The Directional Movement Index (DMI) indicates a bearish trend when the negative directional movement (-DI) crosses above the positive directional movement (+DI).
Bollinger Bands (BB): The price is above the lower Bollinger Band, suggesting a potential reversal from the lower band.
Momentum Indicator: Momentum is negative, indicating increasing selling pressure.
MACD (Moving Average Convergence Divergence): The MACD line is below the signal line, signaling bearish momentum.
Supertrend Indicator: The Supertrend indicator signals a downtrend.
Schaff Trend Cycle (STC): The STC indicates a bearish trend.
Aroon Indicator: The Aroon indicator signals a bearish trend or crossover.
When all these conditions align, the strategy considers it an opportune moment to enter a short trade.
Disclaimer:
THIS ISN'T AN OPTIMAL STRATEGY AT ALL! It was just an old project from when I started learning pine script!
The backtest doesn't promise the same results in the future, always do both in-sample and out-of-sample testing when backtesting a strategy. And make sure you forward test it as well before implementing it!
Furthermore this strategy uses both trend and mean-reversion systems, that is usually a no-go if you want to build robust trend systems .
Don't hesitate to comment if you have any questions or if you have some good notes for a beginner.
Aroon and ASH strategy - ETHERIUM [IkkeOmar]Intro:
This post introduces a Pine Script strategy, as an example if anyone needs a push to get started. This example is a strategy on ETH, obviously it isn't a good strategy, and I wouldn't share my own good strategies because of alpha decay. This strategy combines two technical indicators: Aroon and Absolute Strength Histogram (ASH).
Overview:
The strategy employs the Aroon indicator alongside the Absolute Strength Histogram (ASH) to determine market trends and potential trade setups. Aroon helps identify the strength and direction of a trend, while ASH provides insights into the strength of momentum. By combining these indicators, the strategy aims to capture profitable trading opportunities in Ethereum markets. Normally when developing strats using indicators, you want to find some good indicators, but you NEED to understand their strengths and weaknesses, other indicators can be incorporated to minimize the downs of another indicator. Try to look for synergy in your indicators!
Indicator settings:
Aroon Indicator:
- Two sets of parameters are used for the Aroon indicator:
- For Long Positions: Aroon periods are set to 56 (upper) and 20 (lower).
- For Short Positions: Aroon periods are set to 17 (upper) and 55 (lower).
Absolute Strength Histogram (ASH):
ASH is calculated with a length of 9 bars using the closing price as the data source.
Trading Conditions:
The strategy incorporates specific conditions to initiate and exit trades:
Start Date:
Traders can specify the start date for backtesting purposes.
Trade Direction:
Traders can select the desired trade direction: Long, Short, or Both.
Entry and Exit Conditions:
1. Long Position Entry: A long position is initiated when the Aroon indicator crosses over (crossover) the lower Aroon threshold, indicating a potential uptrend.
2. Long Position Exit: A long position is closed when the Aroon indicator crosses under (crossunder) the lower Aroon threshold.
3. Short Position Entry: A short position is initiated when the Aroon indicator crosses under (crossunder) the upper Aroon threshold, signaling a potential downtrend.
4. Short Position Exit: A short position is closed when the Aroon indicator crosses over (crossover) the upper Aroon threshold.
Disclaimer:
THIS ISN'T AN OPTIMAL STRATEGY AT ALL! It was just an old project from when I started learning pine script!
The backtest doesn't promise the same results in the future, always do both in-sample and out-of-sample testing when backtesting a strategy. And make sure you forward test it as well before implementing it!
Backtesting ModuleDo you often find yourself creating new 'strategy()' scripts for each trading system? Are you unable to focus on generating new systems due to fatigue and time loss incurred in the process? Here's a potential solution: the 'Backtesting Module' :)
INTRODUCTION
Every trading system is based on four basic conditions: long entry, long exit, short entry and short exit (which are typically defined as boolean series in Pine Script).
If you can define the conditions generated by your trading system as a series of integers, it becomes possible to use these variables in different scripts in efficient ways. (Pine Script is a convenient language that allows you to use the integer output of one indicator as a source in another.)
The 'Backtesting Module' is a dynamic strategy script designed to adapt to your signals. It boasts two notable features:
⮞ It produces a backtest report using the entry and exit variables you define.
⮞ It not only serves for system testing but also to combine independent signals into a single system. (This functionality enables to create complex strategies and report on their success!)
The module tests Golden and Death cross signals by default, when you enter your own conditions the default signals will be neutralized. The methodology is described below.
PREPARATION
There are three simple steps to connect your own indicator to the Module.
STEP 1
Firstly, you must define entry and exit variables in your own script. Let's elucidate it with a straightforward example. Consider a system generating long and short signals based on the intersections of two moving averages. Consequently, our conditions would be as follows:
// Signals
long = ta.crossover(ta.sma(close, 14), ta.sma(close, 28))
short = ta.crossunder(ta.sma(close, 14), ta.sma(close, 28))
Now, the question is: How can we convert boolean variables into integer variables? The answer is conditional ternary block, defined as follows:
// Entry & Exit
long_entry = long ? 1 : 0
long_exit = short ? 1 : 0
short_entry = short ? 1 : 0
short_exit = long ? 1 : 0
The mechanics of the Entry & Exit variables are simple. The variable takes on a value of 1 when your trading system generates the signal and if your system does not produce any signal, variable returns 0. In this example, you see how exit signals can be generated in a trading system that only contains entry signals. If you have a system with original exit signals, you can also use them directly. (Please mind the NOTES section below).
STEP 2
To utilize the Entry & Exit variables as source in another script, they must be plotted on the chart. Therefore, the final detail to include in the script containing your trading system would be as follows:
// Plot The Output
plot(long_entry, "Long Entry", display=display.data_window, editable=false)
plot(long_exit, "Long Exit", display=display.data_window, editable=false)
plot(short_entry, "Short Entry", display=display.data_window, editable=false)
plot(short_exit, "Short Exit", display=display.data_window, editable=false)
STEP 3
Now, we are ready to test the system! Load the Backtesting Module indicator onto the chart along with your trading system/indicator. Then set the outputs of your system (Long Entry, Long Exit, Short Entry, Short Exit) as source in the module. That's it.
FEATURES & ORIGINALITY
⮞ Primarily, this script has been created to provide you with an easy and practical method when testing your trading system.
⮞ I thought it might be nice to visualize a few useful results. The Backtesting Module provides insights into the outcomes of both long and short trades by computing the number of trades and the success percentage.
⮞ Through the 'Trade' parameter, users can specify the market direction in which the indicator is permitted to initiate positions.
⮞ Users have the flexibility to define the date range for the test.
⮞ There are optional features allowing users to plot entry prices on the chart and customize bar colors.
⮞ The report and the test date range are presented in a table on the chart screen. The entry price can be monitored in the data window.
⮞ Note that results are based on realized returns, and the open trade is not included in the displayed results. (The only exception is the 'Unrealized PNL' result in the table.)
STRATEGY SETTINGS
The default parameters are as follows:
⮞ Initial Balance : 10000 (in units of currency)
⮞ Quantity : 10% of equity
⮞ Commission : 0.04%
⮞ Slippage : 0
⮞ Dataset : All bars in the chart
For a realistic backtest result, you should size trades to only risk sustainable amounts of equity. Do not risk more than 5-10% on a trade. And ALWAYS configure your commission and slippage parameters according to pessimistic scenarios!
NOTES
⮞ This script is intended solely for development purposes. And it'll will be available for all the indicators I publish.
⮞ In this version of the module, all order types are designed as market orders. The exit size is the sum of the entry size.
⮞ As your trading conditions grow more intricate, you might need to define the outputs of your system in alternative ways. The method outlined in this description is tailored for straightforward signal structures.
⮞ Additionally, depending on the structure of your trading system, the backtest module may require further development. This encompasses stop-loss, take-profit, specific exit orders, quantity, margin and risk management calculations. I am considering releasing improvements that consider these options in future versions.
⮞ An example of how complex trading signals can be generated is the OTT Collection. If you're interested in seeing how the signals are constructed, you can use the link below.
THANKS
Special thanks to PineCoders for their valuable moderation efforts.
I hope this will be a useful example for the TradingView community...
DISCLAIMER
This is just an indicator, nothing more. It is provided for informational and educational purposes exclusively. The utilization of this script does not constitute professional or financial advice. The user solely bears the responsibility for risks associated with script usage. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
