Optimize strategies (#23)

* Update Tradingbox + set limit price

* Change discord message
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Oda
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# Strategy Combination System
## Overview
The improved `TradingBox` strategy combination system provides sophisticated logic for combining multiple trading strategies with different purposes and behaviors. The system is designed around three distinct strategy types, each serving a specific role in the trading decision process.
## Strategy Types
### 1. Signal Strategies (`SignalType.Signal`)
- **Purpose**: Generate rare, precise entry signals when specific conditions are met
- **Behavior**: Sparse signal generation - only fire when opportunity is detected
- **Examples**: RSI Divergence, MACD Cross, Chandelier Exit
- **Weight**: High priority in final decision
### 2. Trend Strategies (`SignalType.Trend`)
- **Purpose**: Identify overall market direction on every candlestick
- **Behavior**: Continuous output (Long/Short/Neutral for each candle)
- **Examples**: EMA Trend, SuperTrend, Moving Average Cross
- **Weight**: Supporting role, validates signal direction
### 3. Context Strategies (`SignalType.Context`)
- **Purpose**: Provide market context to prevent trades in unfavorable conditions
- **Behavior**: Act as filters/guards (allow/block trades)
- **Examples**: Volatility filters, Volume filters, Time-based restrictions
- **Weight**: Veto power - can block otherwise valid signals
## Combination Logic
### Basic Flow
1. **Context Validation**: All context strategies must allow the trade (configurable)
2. **Signal Analysis**: Evaluate entry signals using majority voting
3. **Trend Analysis**: Determine overall market direction
4. **Final Decision**: Combine signal and trend with confidence scoring
### Decision Matrix
| Signal Direction | Trend Direction | Result | Confidence |
|-----------------|----------------|---------|------------|
| Long/Short | Same | Execute | High |
| Long/Short | Neutral/None | Execute | Medium |
| Long/Short | Opposite | Execute* | Low |
| None | Long/Short | Execute | Medium/Low |
| None | None | No Trade | N/A |
*_Depends on `AllowSignalTrendOverride` configuration_
## Configuration Options
The system uses `StrategyComboConfig` to customize behavior:
```csharp
var config = new StrategyComboConfig
{
// Trend agreement thresholds
TrendStrongAgreementThreshold = 0.66m, // 66% for strong trend
// Signal agreement thresholds
SignalAgreementThreshold = 0.5m, // 50% majority for signals
// Override behavior
AllowSignalTrendOverride = true, // Allow signals to override trends
// Quality filters
MinimumConfidence = Confidence.Low, // Minimum confidence to trade
MinimumContextConfidence = Confidence.Medium, // Minimum context quality required
// Defaults
DefaultExchange = TradingExchanges.Binance
};
```
### Context Strategy Behavior
Context strategies now work with **confidence levels** rather than allow/block decisions:
- **High Confidence**: Ideal market conditions (e.g., low volatility)
- **Medium Confidence**: Normal market conditions
- **Low Confidence**: Elevated risk conditions (trade with caution)
- **No Confidence**: Poor conditions (blocks trading)
All context strategies must meet the `MinimumContextConfidence` threshold for trades to proceed.
## Usage Examples
### Basic Usage (Default Configuration)
```csharp
var signal = TradingBox.GetSignal(candles, strategies, previousSignals);
```
### Advanced Usage (Custom Configuration)
```csharp
var config = new StrategyComboConfig
{
TrendStrongAgreementThreshold = 0.75m, // Require 75% trend agreement
MinimumConfidence = Confidence.Medium, // Only trade medium+ confidence
MinimumContextConfidence = Confidence.High, // Only trade in ideal conditions
AllowSignalTrendOverride = false // Don't allow trend conflicts
};
var signal = TradingBox.GetSignal(candles, strategies, previousSignals, config);
```
### Typical Strategy Combinations
#### Conservative Setup
- 1 Signal Strategy (RSI Divergence)
- 2 Trend Strategies (EMA Trend, SuperTrend)
- 1 Context Strategy (Volatility Filter)
#### Aggressive Setup
- 2 Signal Strategies (MACD Cross, Chandelier Exit)
- 1 Trend Strategy (EMA Trend)
- No Context Strategies
#### Scalping Setup
- 3 Signal Strategies (Quick signals)
- 1 Trend Strategy (Short-term trend)
- 2 Context Strategies (Volume + Time filters)
## Key Improvements
### 1. **Flexible Requirements**
- No longer requires ALL strategies to produce signals
- Different logic for different strategy types
- Configurable thresholds and behavior
### 2. **Intelligent Voting**
- Majority voting for signals and trends
- Weighted decisions based on strategy type
- Handles neutral/conflicting signals gracefully
### 3. **Confidence Scoring**
- Dynamic confidence based on agreement levels
- Considers signal-trend alignment
- Configurable minimum confidence thresholds
### 4. **Context Awareness**
- Context strategies provide market condition quality through confidence levels
- Configurable minimum context confidence thresholds
- Simplified logic: all context strategies must meet minimum confidence
- No more complex unanimous vs. majority requirements
### 5. **Trend Reversal Support**
- Signal strategies can override trend direction
- Useful for catching trend reversals
- Configurable override behavior
## Best Practices
### Strategy Selection
1. **Use 1-2 Signal strategies** for entry precision
2. **Use 1-3 Trend strategies** for direction confirmation
3. **Use 0-2 Context strategies** for risk management
### Configuration Tuning
1. **Start with defaults** and adjust based on backtesting
2. **Higher thresholds** = fewer but higher quality signals
3. **Lower thresholds** = more signals but potentially more noise
### Testing Recommendations
1. **Backtest different configurations** on historical data
2. **Monitor confidence distributions** in live trading
3. **Adjust thresholds** based on performance metrics
## Migration from Old System
The new system is backward compatible:
- Existing calls to `GetSignal()` work unchanged
- Old unanimous agreement logic is still available via configuration
- New overloads provide additional control when needed
## Performance Considerations
- Strategy evaluation is optimized for efficiency
- Signal caching prevents duplicate calculations
- Configurable quality filters reduce unnecessary trades
- Separate validation logic for different strategy types

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# Volatility-Controlled Trading Scenario
## Overview
This scenario combines the **STCStrategy** (Signal) with the **StDevContext** (Context) to create a sophisticated trading system that identifies trend reversals while respecting market volatility conditions.
## Strategy Components
### STCStrategy (Signal Type)
- **Purpose**: Detects trend reversals using Schaff Trend Cycle indicator
- **Signals**:
- Long when STC crosses above 25 (oversold recovery)
- Short when STC crosses below 75 (overbought decline)
- **Confidence**: Medium for all signals
### StDevContext (Context Type)
- **Purpose**: Evaluates market volatility to determine trading conditions
- **Z-Score Ranges**:
- `|Z| ≤ 0.5`: **High Confidence** - Very stable conditions, ideal for trading
- `0.5 < |Z| ≤ 1.0`: **Medium Confidence** - Normal volatility, good for trading
- `1.0 < |Z| ≤ 1.5`: **Low Confidence** - Elevated volatility, trade with caution
- `|Z| > 1.5`: **No Confidence** - High volatility, avoid trading
## Implementation
### Basic Setup
```csharp
// Initialize strategies
var stcStrategy = new STCStrategy(
name: "STC Reversal",
cyclePeriods: 10, // Cycle length for STC calculation
fastPeriods: 12, // Fast EMA period
slowPeriods: 26 // Slow EMA period
);
var volatilityFilter = new StDevContext(
name: "Volatility Filter",
period: 20 // Period for standard deviation calculation
);
// Set up strategies
var strategies = new HashSet<IStrategy> { stcStrategy, volatilityFilter };
```
### Configuration Options
```csharp
// Conservative configuration (safer trading)
var conservativeConfig = new StrategyComboConfig
{
MinimumContextConfidence = Confidence.High, // Only trade in very stable conditions
MinimumConfidence = Confidence.Medium, // Only trade medium+ confidence signals
AllowSignalTrendOverride = true // Allow reversal signals
};
// Aggressive configuration (more trading opportunities)
var aggressiveConfig = new StrategyComboConfig
{
MinimumContextConfidence = Confidence.Low, // Trade in elevated volatility
MinimumConfidence = Confidence.Low, // Accept lower confidence signals
AllowSignalTrendOverride = true
};
// Balanced configuration (recommended starting point)
var balancedConfig = new StrategyComboConfig
{
MinimumContextConfidence = Confidence.Medium, // Require normal volatility conditions
MinimumConfidence = Confidence.Medium, // Standard confidence requirements
AllowSignalTrendOverride = true // Allow trend reversals
};
```
### Trading Logic
```csharp
public Signal GetTradingSignal(HashSet<Candle> candles, StrategyComboConfig config)
{
// Update strategies with latest candle data
foreach (var strategy in strategies)
{
strategy.UpdateCandles(candles);
}
// Get combined signal
var signal = TradingBox.GetSignal(
candles,
strategies,
new HashSet<Signal>(), // Previous signals
config,
loopbackPeriod: 1 // Check only latest candle
);
return signal;
}
```
### Complete Example
```csharp
public class VolatilityControlledTradingBot
{
private readonly STCStrategy _stcStrategy;
private readonly StDevContext _volatilityFilter;
private readonly StrategyComboConfig _config;
public VolatilityControlledTradingBot()
{
_stcStrategy = new STCStrategy("STC Reversal", 10, 12, 26);
_volatilityFilter = new StDevContext("Volatility Filter", 20);
_config = new StrategyComboConfig
{
MinimumContextConfidence = Confidence.Medium,
MinimumConfidence = Confidence.Medium,
AllowSignalTrendOverride = true
};
}
public async Task<Signal> AnalyzeMarket(List<Candle> candles)
{
// Ensure we have enough data
if (candles.Count < 50) // Need sufficient history for both strategies
return null;
var strategies = new HashSet<IStrategy> { _stcStrategy, _volatilityFilter };
// Update strategies
foreach (var strategy in strategies)
{
strategy.UpdateCandles(candles.ToHashSet());
}
// Get trading signal
var signal = TradingBox.GetSignal(
candles.ToHashSet(),
strategies,
new HashSet<Signal>(),
_config
);
// Log decision process
if (signal != null)
{
Console.WriteLine($"Trade Signal Generated:");
Console.WriteLine($" Direction: {signal.Direction}");
Console.WriteLine($" Confidence: {signal.Confidence}");
Console.WriteLine($" Date: {signal.Date}");
}
else
{
Console.WriteLine("No trade signal - conditions not met");
}
return signal;
}
}
```
## Loopback Period Recommendations
### For this Scenario:
- **Loopback Period: 1-3 candles**
- STC reversals are typically captured immediately
- Volatility context is current market condition
- Short loopback prevents stale signals
### Considerations:
- **Market Timeframe**:
- 5m-15m charts: Use loopback = 1
- 1h-4h charts: Use loopback = 2-3
- Daily charts: Use loopback = 1-2
## Expected Behavior
### High Volatility Conditions (Z-Score > 1.5)
- StDevContext returns `Confidence.None`
- No trades executed regardless of STC signals
- Protects against whipsaw movements
### Normal Volatility Conditions (|Z-Score| ≤ 1.0)
- StDevContext returns `Confidence.Medium` or `Confidence.High`
- STC signals are evaluated normally
- Trades executed when STC crosses thresholds
### Low Volatility Conditions (|Z-Score| ≤ 0.5)
- StDevContext returns `Confidence.High`
- Ideal conditions for trend reversal trading
- Higher probability of successful STC signals
## Performance Tuning
### STC Parameters:
- **cyclePeriods**: 8-15 (shorter = more sensitive)
- **fastPeriods**: 9-15 (standard EMA settings)
- **slowPeriods**: 21-30 (standard EMA settings)
### Volatility Parameters:
- **period**: 14-21 (standard volatility lookback)
- **Z-Score thresholds**: Adjust based on asset volatility characteristics
## Risk Management
1. **Position Sizing**: Reduce size when volatility confidence is low
2. **Stop Losses**: Tighter stops in high volatility periods
3. **Take Profits**: Quicker profit-taking when volatility increases
4. **Market Hours**: Consider time-based context strategies for optimal trading windows
## Backtesting Recommendations
1. Test different volatility thresholds on historical data
2. Evaluate performance across various market conditions
3. Monitor the distribution of context confidence levels
4. Analyze signal quality vs. market volatility correlation
5. Compare performance with and without volatility filtering