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https://github.com/triqbit/mt5-ai-xauusd-trader.git
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43f26473e1
* ⚙️ Jules02: Performance and runtime analysis — Optimizing database I/O and UI rendering - Optimized DecisionSupportSystem.format_for_operator to return early after printing to console, bypassing expensive capture() calls. - Refactored TradeLogger.read_performance_report to make database persistence optional, defaulting to False. - Updated main.py to only persist performance metrics when a trade is closed, reducing redundant DB writes in the trading loop. - Added benchmark_performance.py to provide measurable evidence of performance gains. Co-authored-by: xnessom <255288032+xnessom@users.noreply.github.com> * ⚙️ Jules02: Performance and runtime analysis — Final optimizations and CI fixes - Optimized DecisionSupportSystem to bypass redundant UI rendering when printing to console. - Refactored TradeLogger to only persist performance metrics when requested (on trade close). - Fixed CI Lint Error (B904) in schemas.py by using 'raise ... from err'. - Added and stabilized performance optimization tests. - Updated PERFORMANCE_TUNING.md with new optimization documentation. Co-authored-by: xnessom <255288032+xnessom@users.noreply.github.com> --------- Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: xnessom <255288032+xnessom@users.noreply.github.com>
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
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import time
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import pandas as pd
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import numpy as np
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from src.core.feature_engineering import FeatureEngineer
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from src.core.trade_logger import TradeLogger
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import os
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def benchmark_feature_engineering():
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print("Benchmarking Feature Engineering...")
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# Create 500 bars of dummy data
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data = {
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"open": np.random.randn(500) + 2000,
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"high": np.random.randn(500) + 2002,
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"low": np.random.randn(500) + 1998,
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"close": np.random.randn(500) + 2000,
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"tick_volume": np.random.randint(100, 1000, 500)
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}
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df = pd.DataFrame(data)
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df.index = pd.date_range(start="2024-01-01", periods=500, freq="5min")
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fe = FeatureEngineer(base_timeframe="M5")
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start = time.perf_counter()
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for _ in range(10):
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_ = fe.compute_features(df)
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end = time.perf_counter()
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print(f"Average Feature Engineering time: {(end - start) / 10 * 1000:.2f}ms")
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def benchmark_trade_logger():
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print("\nBenchmarking TradeLogger Performance Report...")
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db_path = "benchmark_trades.db"
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if os.path.exists(db_path):
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os.remove(db_path)
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logger = TradeLogger(db_url=f"sqlite:///{db_path}")
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# Insert 1000 closed trades
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print("Inserting 1000 trades...")
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for i in range(1000):
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ticket = 10000 + i
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logger.log_trade(ticket, "XAUUSD", 1, 2000.0, 0.1)
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logger.update_trade(ticket, 2010.0, pnl=100.0 if i % 2 == 0 else -80.0)
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start = time.perf_counter()
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for _ in range(20):
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_ = logger.read_performance_report()
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end = time.perf_counter()
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print(f"Average Performance Report time (1000 trades): {(end - start) / 20 * 1000:.2f}ms")
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if os.path.exists(db_path):
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os.remove(db_path)
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if __name__ == "__main__":
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benchmark_feature_engineering()
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benchmark_trade_logger()
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