/**
* Signal Generation Tools
*
* Provides AI-enhanced trading signal generation, strategy backtesting,
* and ML-based parameter optimization.
*/
// Tool for AI-enhanced signal generation
const generateSignals = {
name: 'generate_signals',
description: 'Generate trading signals using AI-enhanced technical analysis',
parameters: {
type: 'object',
required: ['model', 'marketData'],
properties: {
model: {
type: 'string',
enum: ['LSTM', 'GRU', 'XGBoost', 'Ensemble'],
description: 'Machine learning model to use for prediction'
},
marketData: {
type: 'object',
properties: {
symbol: { type: 'string' },
timeframe: { type: 'string' },
exchange: { type: 'string' }
},
description: 'Market data parameters'
},
confidence: {
type: 'number',
description: 'Minimum confidence threshold (0-1, default: 0.7)'
}
}
},
handler: async ({ model, marketData, confidence = 0.7 }) => {
try {
// Demo implementation - would use real ML models in production
const signals = [
{
symbol: marketData.symbol || 'BTC/USDT',
exchange: marketData.exchange || 'binance',
timeframe: marketData.timeframe || '4h',
signal_type: 'STRONG_BUY',
confidence: 0.89,
timestamp: Date.now(),
price: 67850.23,
indicators: {
RSI: { value: 35.2, signal: 'OVERSOLD' },
MACD: { signal: 'BULLISH_CROSS', histogram: 0.0034 },
BB: { position: 'LOWER_BAND', squeeze: true },
Volume: { relative_to_avg: 2.4 }
},
model_used: model,
risk_reward_ratio: 4.2,
target_price: 71250.50,
stop_loss: 66950.10,
recommended_position_size: '3% of portfolio',
analysis: "Strong buy signal confirmed by oversold RSI, MACD bullish crossover, and price at lower Bollinger Band with increased volume. Recent dip presents favorable entry point with positive risk-reward ratio."
},
{
symbol: 'ETH/USDT',
exchange: 'binance',
timeframe: '4h',
signal_type: 'BUY',
confidence: 0.82,
timestamp: Date.now(),
price: 3247.85,
indicators: {
RSI: { value: 42.3, signal: 'NEUTRAL' },
MACD: { signal: 'BULLISH_DIVERGENCE', histogram: 0.12 },
BB: { position: 'MIDDLE', squeeze: false },
Volume: { relative_to_avg: 1.5 }
},
model_used: model,
risk_reward_ratio: 3.5,
target_price: 3420.00,
stop_loss: 3180.00,
recommended_position_size: '2% of portfolio',
analysis: "Buy signal based on bullish MACD divergence and increasing volume. Price forming support at key level with potential upside target at previous resistance."
}
];
// Filter by minimum confidence
const filteredSignals = signals.filter(signal => signal.confidence >= confidence);
return {
signals: filteredSignals,
timestamp: Date.now(),
model: model
};
} catch (error) {
return {
error: true,
message: `Failed to generate signals: ${error.message}`
};
}
}
};
// Tool for strategy backtesting
const backtestStrategy = {
name: 'backtest_strategy',
description: 'Backtest a trading strategy against historical data',
parameters: {
type: 'object',
required: ['strategy', 'historicalData'],
properties: {
strategy: {
type: 'object',
properties: {
name: { type: 'string' },
parameters: { type: 'object' }
},
description: 'Trading strategy configuration'
},
historicalData: {
type: 'object',
properties: {
symbol: { type: 'string' },
timeframe: { type: 'string' },
startDate: { type: 'string' },
endDate: { type: 'string' }
},
description: 'Historical data parameters'
},
initialCapital: {
type: 'number',
description: 'Initial capital amount for backtesting'
},
fees: {
type: 'object',
description: 'Fee structure for trading'
}
}
},
handler: async ({ strategy, historicalData, initialCapital = 10000, fees = { maker: 0.1, taker: 0.1 } }) => {
try {
// Demo implementation - would use real backtesting engine in production
const backtestResult = {
strategy: strategy.name,
symbol: historicalData.symbol,
timeframe: historicalData.timeframe,
period: `${historicalData.startDate} to ${historicalData.endDate}`,
performance: {
totalReturn: 38.47,
annualizedReturn: 67.82,
maxDrawdown: -15.3,
sharpeRatio: 1.94,
sortinoRatio: 2.12,
winRate: 74.4,
profitFactor: 2.85
},
trades: {
total: 47,
profitable: 35,
unprofitable: 12,
averageProfitPercentage: 4.2,
averageLossPercentage: -2.1,
largestProfit: 12.5,
largestLoss: -5.7,
averageHoldingPeriod: '28.4 hours'
},
equityCurve: {
initial: initialCapital,
final: initialCapital * (1 + 38.47/100),
timestamps: Array.from({ length: 10 }, (_, i) => {
return {
date: new Date(Date.now() - (9-i) * 30 * 24 * 60 * 60 * 1000).toISOString().split('T')[0],
equity: initialCapital * (1 + (38.47/100) * (i+1)/10)
};
})
},
bestPerformingPeriod: {
start: '2024-03-15',
end: '2024-04-01',
return: 17.8,
trades: 9
},
worstPerformingPeriod: {
start: '2024-01-10',
end: '2024-01-25',
return: -8.3,
trades: 5
},
marketComparisonReturn: 12.3, // Buy and hold return
recommendations: [
"Increase position size during high-conviction setups",
"Tighten stop-loss during high volatility periods",
"Consider taking partial profits at resistance levels"
]
};
return backtestResult;
} catch (error) {
return {
error: true,
message: `Failed to backtest strategy: ${error.message}`
};
}
}
};
// Tool for ML-based strategy optimization
const optimizeStrategy = {
name: 'optimize_strategy',
description: 'Optimize trading strategy parameters using machine learning',
parameters: {
type: 'object',
required: ['strategy', 'optimizationGoal'],
properties: {
strategy: {
type: 'object',
properties: {
name: { type: 'string' },
parameters: { type: 'object' }
},
description: 'Trading strategy to optimize'
},
optimizationGoal: {
type: 'string',
enum: ['maximizeReturn', 'maximizeSharpe', 'minimizeDrawdown', 'balanced'],
description: 'Optimization objective'
},
optimizationMethod: {
type: 'string',
enum: ['genetic', 'bayesian', 'grid', 'random'],
description: 'Optimization method (default: bayesian)'
},
testPeriod: {
type: 'object',
properties: {
startDate: { type: 'string' },
endDate: { type: 'string' }
},
description: 'Testing period for optimization'
}
}
},
handler: async ({ strategy, optimizationGoal, optimizationMethod = 'bayesian', testPeriod }) => {
try {
// Demo implementation - would use real optimization engine in production
const optimizationResult = {
strategy: strategy.name,
originalParameters: strategy.parameters,
optimizedParameters: {
// Example for RSI strategy
rsiPeriod: 14,
overboughtThreshold: 72,
oversoldThreshold: 32,
macdFastPeriod: 9,
macdSlowPeriod: 21,
macdSignalPeriod: 9,
stopLossPercentage: 3.2,
takeProfitPercentage: 8.5,
trailingStopActivation: 4.0,
trailingStopDistance: 2.5,
positionSizingMethod: 'volatility-adjusted'
},
improvementMetrics: {
returnChange: '+8.4%',
sharpeRatioChange: '+0.32',
drawdownChange: '-3.1%',
winRateChange: '+5.2%'
},
optimizationStatistics: {
iterationsRun: 250,
optimizationTime: '18 minutes',
parameterImportance: {
rsiPeriod: 15,
oversoldThreshold: 28,
stopLossPercentage: 24,
takeProfitPercentage: 18,
other: 15
}
},
validationResults: {
outOfSamplePerformance: {
return: 12.8,
sharpeRatio: 1.65,
maxDrawdown: -9.8,
winRate: 69
},
robustnessScore: 85, // 0-100
overfittingRisk: 'LOW'
},
recommendedSettings: {
marketConditions: 'All',
bestTimeframes: ['4h', '1d'],
suitableAssets: ['BTC', 'ETH', 'Large caps']
}
};
return optimizationResult;
} catch (error) {
return {
error: true,
message: `Failed to optimize strategy: ${error.message}`
};
}
}
};
// Tool for pattern detection and market scanning
const detectPatterns = {
name: 'detect_patterns',
description: 'Detect technical chart patterns across multiple assets',
parameters: {
type: 'object',
required: ['patterns', 'timeframes'],
properties: {
patterns: {
type: 'array',
items: {
type: 'string',
enum: ['Head_And_Shoulders', 'Double_Top', 'Double_Bottom', 'Triangle', 'Rectangle', 'Flag', 'Cup_And_Handle', 'Wedge']
},
description: 'Patterns to detect'
},
timeframes: {
type: 'array',
items: { type: 'string' },
description: 'Timeframes to analyze'
},
minConfidence: {
type: 'number',
description: 'Minimum confidence threshold (0-1)'
}
}
},
handler: async ({ patterns, timeframes, minConfidence = 0.7 }) => {
try {
// Demo implementation - would use real pattern recognition in production
const patternMatches = [
{
symbol: 'BTC/USDT',
pattern: 'Bull_Flag',
timeframe: '4h',
confidence: 0.86,
formationStart: '2024-07-01',
formationEnd: '2024-07-05',
breakoutLevel: 68500,
targetPrice: 73200,
stopLoss: 66900,
volume: {
duringFormation: 'Decreasing',
atBreakout: 'Increasing'
},
tradingRecommendation: "Buy on breakout of 68500 with stop at 66900. Target: 73200"
},
{
symbol: 'ETH/USDT',
pattern: 'Cup_And_Handle',
timeframe: '1d',
confidence: 0.78,
formationStart: '2024-06-01',
formationEnd: '2024-07-04',
breakoutLevel: 3350,
targetPrice: 3750,
stopLoss: 3150,
volume: {
duringFormation: 'Consistent',
atBreakout: 'Pending'
},
tradingRecommendation: "Watch for breakout above 3350 with increasing volume"
}
];
// Filter by minimum confidence
const filteredPatterns = patternMatches.filter(match => match.confidence >= minConfidence);
return {
patterns: filteredPatterns,
timestamp: Date.now(),
patternsSearched: patterns,
timeframesAnalyzed: timeframes,
totalMatchesFound: filteredPatterns.length
};
} catch (error) {
return {
error: true,
message: `Failed to detect patterns: ${error.message}`
};
}
}
};
module.exports = {
generateSignals,
backtestStrategy,
optimizeStrategy,
detectPatterns
};
