remove: V10 momentum system - backtest proved it adds no value
- Removed v10 TradingView indicator (moneyline_v10_momentum_dots.pinescript) - Removed v10 penalty system from signal-quality.ts (-30/-25 point penalties) - Removed backtest result files (sweep_*.csv) - Updated copilot-instructions.md to remove v10 references - Simplified direction-specific quality thresholds (LONG 90+, SHORT 80+) Rationale: - 1,944 parameter combinations tested in backtest - All top results IDENTICAL (568 trades, $498 P&L, 61.09% WR) - Momentum parameters had ZERO impact on trade selection - Profit factor 1.027 too low (barely profitable after fees) - Max drawdown -$1,270 vs +$498 profit = terrible risk-reward - v10 penalties were blocking good trades (bug: applied to wrong positions) Keeping v9 as production system - simpler, proven, effective.
This commit is contained in:
mindesbunister
161
scripts/export_binance_ohlcv.py
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161
scripts/export_binance_ohlcv.py
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#!/usr/bin/env python3
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"""Download historical OHLCV data from Binance and store it as CSV."""
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from __future__ import annotations
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import argparse
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import json
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import sys
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import time
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from pathlib import Path
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from typing import Dict, List
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from urllib.parse import urlencode
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from urllib.request import Request, urlopen
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import pandas as pd
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BINANCE_REST_BASE = "https://api.binance.com/api/v3/klines"
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MAX_LIMIT = 1000
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# Interval → milliseconds mapping taken from Binance documentation
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INTERVAL_MS: Dict[str, int] = {
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"1m": 60_000,
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"3m": 180_000,
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"5m": 300_000,
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"15m": 900_000,
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"30m": 1_800_000,
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"1h": 3_600_000,
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"2h": 7_200_000,
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"4h": 14_400_000,
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"6h": 21_600_000,
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"8h": 28_800_000,
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"12h": 43_200_000,
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"1d": 86_400_000,
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}
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Export OHLCV candles from Binance")
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parser.add_argument("--symbol", default="SOLUSDT", help="Binance symbol, e.g. SOLUSDT")
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parser.add_argument(
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"--interval",
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default="5m",
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choices=sorted(INTERVAL_MS.keys()),
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help="Binance interval (default: 5m)",
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)
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parser.add_argument(
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"--start",
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required=True,
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help="Start timestamp (ISO 8601, e.g. 2024-01-01 or 2024-01-01T00:00:00Z)",
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)
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parser.add_argument(
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"--end",
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required=True,
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help="End timestamp (ISO 8601, inclusive)",
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)
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parser.add_argument(
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"--output",
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type=Path,
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required=True,
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help="Path to output CSV file (directories created automatically)",
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)
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parser.add_argument(
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"--rate-limit-wait",
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type=float,
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default=0.2,
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help="Seconds to sleep between paginated requests (default: 0.2s)",
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)
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return parser.parse_args()
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def to_millis(value: str) -> int:
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ts = pd.Timestamp(value)
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if ts.tzinfo is None:
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ts = ts.tz_localize("UTC")
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else:
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ts = ts.tz_convert("UTC")
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return int(ts.timestamp() * 1000)
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def fetch_chunk(symbol: str, interval: str, start_ms: int, end_ms: int) -> List[List[float]]:
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params = {
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"symbol": symbol.upper(),
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"interval": interval,
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"startTime": start_ms,
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"endTime": end_ms,
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"limit": MAX_LIMIT,
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}
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query = urlencode(params)
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url = f"{BINANCE_REST_BASE}?{query}"
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req = Request(url, headers={"User-Agent": "binance-export/1.0"})
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with urlopen(req, timeout=30) as resp:
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payload = resp.read()
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data = json.loads(payload)
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if isinstance(data, dict) and data.get("code"):
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raise RuntimeError(f"Binance error {data['code']}: {data.get('msg')}")
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return data # type: ignore[return-value]
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def main() -> int:
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args = parse_args()
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interval = args.interval
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if interval not in INTERVAL_MS:
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raise ValueError(f"Unsupported interval: {interval}")
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start_ms = to_millis(args.start)
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end_ms = to_millis(args.end)
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if end_ms <= start_ms:
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raise ValueError("End time must be after start time")
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interval_ms = INTERVAL_MS[interval]
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cursor = start_ms
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rows: List[List[float]] = []
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request_count = 0
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while cursor < end_ms:
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chunk = fetch_chunk(args.symbol, interval, cursor, end_ms)
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if not chunk:
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break
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rows.extend(chunk)
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request_count += 1
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last_open = chunk[-1][0]
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cursor = last_open + interval_ms
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if len(chunk) < MAX_LIMIT:
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break
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time.sleep(args.rate_limit_wait)
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if not rows:
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raise RuntimeError("No data returned from Binance")
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columns = [
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"open_time",
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"open",
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"high",
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"low",
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"close",
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"volume",
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"close_time",
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"quote_asset_volume",
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"number_of_trades",
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"taker_buy_base",
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"taker_buy_quote",
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"ignore",
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]
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df = pd.DataFrame(rows, columns=columns)
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df = df.drop(columns=["close_time", "quote_asset_volume", "ignore"])
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df["timestamp"] = pd.to_datetime(df["open_time"], unit="ms", utc=True).dt.tz_convert(None)
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df = df[["timestamp", "open", "high", "low", "close", "volume", "number_of_trades", "taker_buy_base", "taker_buy_quote"]]
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df[["open", "high", "low", "close", "volume"]] = df[["open", "high", "low", "close", "volume"]].astype(float)
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args.output.parent.mkdir(parents=True, exist_ok=True)
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df.to_csv(args.output, index=False)
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first = df.iloc[0].timestamp
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last = df.iloc[-1].timestamp
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duration_days = (last - first).days
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print(f"Saved {len(df):,} candles for {args.symbol} ({interval}) spanning ~{duration_days} days")
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print(f"Requests made: {request_count}")
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print(f"Output: {args.output}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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273
scripts/run_backtest_sweep.py
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273
scripts/run_backtest_sweep.py
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@@ -0,0 +1,273 @@
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#!/usr/bin/env python3
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"""Parameter sweep utility for Money Line backtests."""
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from __future__ import annotations
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import argparse
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from itertools import product
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from multiprocessing import Pool
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from pathlib import Path
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from typing import List, Sequence
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import sys
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import time
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.append(str(PROJECT_ROOT))
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import pandas as pd
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from backtester.data_loader import load_csv
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from backtester.indicators.money_line import MoneyLineInputs
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from backtester.simulator import TradeConfig, simulate_money_line
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_DATA_SLICE = None
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_TRADE_CONFIG = None
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_GRID_KEYS: Sequence[str] = []
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def parse_float_list(value: str) -> List[float]:
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return [float(item) for item in value.split(",") if item]
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def parse_int_list(value: str) -> List[int]:
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return [int(item) for item in value.split(",") if item]
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def build_arg_parser() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(description="Run Money Line parameter sweeps against a CSV dataset")
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parser.add_argument("--csv", type=Path, required=True, help="Path to CSV with OHLCV data")
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parser.add_argument("--symbol", required=True, help="Symbol label (e.g., SOL-PERP)")
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parser.add_argument("--timeframe", default="5", help="Timeframe label (for reporting only)")
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parser.add_argument("--start", help="Optional ISO start timestamp filter", default=None)
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parser.add_argument("--end", help="Optional ISO end timestamp filter", default=None)
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parser.add_argument("--position-size", type=float, default=8100.0, help="Notional position size per trade")
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parser.add_argument("--max-bars", type=int, default=288, help="Max bars to hold a trade (default 288 = 24h on 5m)")
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parser.add_argument("--top", type=int, default=10, help="How many results to show")
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parser.add_argument(
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"--output",
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type=Path,
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help="Optional CSV to store every combination's metrics",
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)
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parser.add_argument(
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"--flip-thresholds",
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default="0.5,0.6,0.7",
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help="Comma separated flip threshold percentages",
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)
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parser.add_argument(
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"--ma-gap-thresholds",
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default="0.25,0.35,0.45",
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help="Comma separated MA gap thresholds",
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)
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parser.add_argument(
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"--momentum-adx",
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default="22,24,26",
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help="Comma separated ADX minimums for momentum dots",
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)
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parser.add_argument(
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"--momentum-long-pos",
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default="65,70",
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help="Comma separated maximum price position for long momentum entries",
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)
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parser.add_argument(
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"--momentum-short-pos",
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default="30,35",
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help="Comma separated minimum price position for short momentum entries",
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)
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parser.add_argument(
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"--cooldown-bars",
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default="2,3,4",
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help="Comma separated cooldown bar counts between primary flips",
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)
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parser.add_argument(
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"--momentum-spacing",
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default="3,4,5",
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help="Comma separated spacing (bars) between momentum signals",
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)
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parser.add_argument(
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"--momentum-cooldown",
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default="2,3",
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help="Comma separated cooldown (bars) after primary signal before momentum allowed",
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)
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parser.add_argument(
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"--workers",
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type=int,
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default=1,
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help="Number of worker processes (use >1 for multi-core sweeps)",
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)
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parser.add_argument(
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"--limit",
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type=int,
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help="Optional number of combinations to run (preview mode)",
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)
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return parser
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def run_sweep(args: argparse.Namespace) -> pd.DataFrame:
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data_slice = load_csv(args.csv, args.symbol, args.timeframe, start=args.start, end=args.end)
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trade_config = TradeConfig(position_size=args.position_size, max_bars_per_trade=args.max_bars)
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grids = {
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"flip_threshold_percent": parse_float_list(args.flip_thresholds),
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"ma_gap_threshold": parse_float_list(args.ma_gap_thresholds),
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"momentum_min_adx": parse_float_list(args.momentum_adx),
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"momentum_long_max_pos": parse_float_list(args.momentum_long_pos),
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"momentum_short_min_pos": parse_float_list(args.momentum_short_pos),
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"cooldown_bars": parse_int_list(args.cooldown_bars),
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"momentum_spacing": parse_int_list(args.momentum_spacing),
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"momentum_cooldown": parse_int_list(args.momentum_cooldown),
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}
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combos = list(product(*grids.values()))
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total_combos = len(combos)
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print(f"Evaluating {total_combos} combinations...")
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if args.limit is not None and args.limit > 0 and args.limit < total_combos:
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combos = combos[: args.limit]
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print(f"Preview mode: running first {len(combos)} combos (out of {total_combos})")
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keys = list(grids.keys())
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worker_count = args.workers if args.workers and args.workers > 0 else 1
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total_to_run = len(combos)
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progress = ProgressBar(total=total_to_run)
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if total_to_run == 0:
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print("No combinations to evaluate with current configuration.")
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return pd.DataFrame()
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if worker_count <= 1:
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records = []
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for idx, combo in enumerate(combos, start=1):
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records.append(_evaluate_combo(combo, keys, data_slice, trade_config))
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progress.update(idx)
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else:
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print(f"Using {worker_count} worker processes")
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with Pool(
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processes=worker_count,
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initializer=_init_worker,
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initargs=(data_slice, trade_config, keys),
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) as pool:
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records = []
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for idx, record in enumerate(pool.imap_unordered(_worker_eval, combos), start=1):
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records.append(record)
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progress.update(idx)
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progress.finish()
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elapsed = progress.elapsed
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combos_run = len(combos)
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avg_time = elapsed / combos_run if combos_run else 0
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print(
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f"Processed {combos_run} combos in {elapsed:.1f}s (avg {avg_time:.2f}s per combo)"
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)
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if combos_run < total_combos:
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projected = avg_time * total_combos
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print(
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f"Estimated full sweep ({total_combos} combos) would take ~{projected/60:.1f} minutes with current settings"
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)
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return pd.DataFrame(records)
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def _init_worker(data_slice, trade_config, keys):
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global _DATA_SLICE, _TRADE_CONFIG, _GRID_KEYS
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_DATA_SLICE = data_slice
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_TRADE_CONFIG = trade_config
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_GRID_KEYS = keys
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def _worker_eval(combo):
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return _evaluate_combo(combo, _GRID_KEYS, _DATA_SLICE, _TRADE_CONFIG)
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def _evaluate_combo(combo, keys, data_slice, trade_config):
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params = dict(zip(keys, combo))
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inputs = MoneyLineInputs(**params)
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result = simulate_money_line(data_slice.data, data_slice.symbol, inputs=inputs, config=trade_config)
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trades = result.trades
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pnl = result.total_pnl
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win_rate = result.win_rate * 100
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max_dd = result.max_drawdown
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avg_trade = result.average_pnl
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profit_factor = _profit_factor(trades)
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return {
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**params,
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"trades": len(trades),
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"total_pnl": pnl,
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"win_rate": win_rate,
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"avg_pnl": avg_trade,
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"max_drawdown": max_dd,
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"profit_factor": profit_factor,
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}
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class ProgressBar:
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def __init__(self, total: int, bar_length: int = 40) -> None:
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self.total = max(total, 0)
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self.bar_length = bar_length
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self.start_time = time.time()
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self._done = False
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def update(self, count: int) -> None:
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if self.total <= 0:
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return
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count = min(count, self.total)
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percent = count / self.total
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filled = int(self.bar_length * percent)
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bar = "#" * filled + "-" * (self.bar_length - filled)
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elapsed = time.time() - self.start_time
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rate = elapsed / count if count else 0
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remaining = rate * (self.total - count) if rate else 0
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sys.stdout.write(
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f"\r[{bar}] {percent*100:5.1f}% ({count}/{self.total}) | "
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f"Elapsed {elapsed:6.1f}s | ETA {remaining:6.1f}s"
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)
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sys.stdout.flush()
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if count >= self.total:
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self._done = True
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sys.stdout.write("\n")
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def finish(self) -> None:
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if not self._done and self.total > 0:
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self.update(self.total)
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@property
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def elapsed(self) -> float:
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return time.time() - self.start_time
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def _profit_factor(trades) -> float:
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gains = [t.realized_pnl for t in trades if t.realized_pnl > 0]
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losses = [-t.realized_pnl for t in trades if t.realized_pnl < 0]
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total_gain = sum(gains)
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total_loss = sum(losses)
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if total_loss == 0:
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return float("inf") if total_gain > 0 else 0.0
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return total_gain / total_loss
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def main() -> int:
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parser = build_arg_parser()
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args = parser.parse_args()
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df = run_sweep(args)
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df = df.sort_values("total_pnl", ascending=False)
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top_n = df.head(args.top)
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print("\n=== TOP RESULTS ===")
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for idx, row in top_n.iterrows():
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print(
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f"PnL ${row['total_pnl']:.2f} | Trades {row['trades']} | Win {row['win_rate']:.2f}% | "
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f"DD ${row['max_drawdown']:.2f} | PF {row['profit_factor']:.2f} | params {row.drop(['trades','total_pnl','win_rate','avg_pnl','max_drawdown','profit_factor']).to_dict()}"
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)
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if args.output:
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args.output.parent.mkdir(parents=True, exist_ok=True)
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df.to_csv(args.output, index=False)
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print(f"\nSaved all {len(df)} results to {args.output}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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Reference in New Issue
Block a user