trading_bot_v4/docs/BACKTESTER_STATUS.md

Backtester Status (Dec 2025)

Current Scope

• Python package under backtester/ (isolated from trading bot)
• CSV loader (data_loader.py) validates OHLCV inputs with timestamp filtering
• Math utilities (math_utils.py) implement RMA, ATR, ADX helpers
• Money Line indicator module (indicators/money_line.py) provides:
  • Configurable inputs for v9/v10 parity (MoneyLineInputs dataclass)
  • Primary + momentum signal detection (flip threshold, MA gap, ADX/volume filters)
  • Output MoneyLineSignal records with full metric context
• Strategy simulator (simulator.py) that consumes OHLCV data + indicator signals and reproduces the ATR-based TP1/TP2 runner logic (including ADX runner stops, trailing stop, MAE/MFE tracking, and placeholder quality filters)
• Command-line runner (cli.py) to execute quick tests against CSV data, print performance stats, and export trade logs
• Binance OHLC exporter (scripts/export_binance_ohlcv.py) for refreshing SOL/USDT candle sets straight from the public REST API

Next Steps

1. Wire production quality scoring + adaptive leverage gating into the simulator so backtests align with live trade filters
2. Build batching utilities for multi-symbol / multi-parameter sweeps and persist the results to CSV/Parquet for analysis
3. Compare Binance-derived backtests vs. Drift trade history to calibrate slippage/latency assumptions
4. Extend CLI to accept saved indicator scores from the database once we export them (blocked signals, 1m data feed)

Simulator Plan

• Core engine (backtester/simulator.py): iterate through OHLCV rows, feed them to the Money Line indicator, and execute trades using the same ATR TP1/TP2 runner + adaptive leverage stack used in production.
• Position model: store entry price, size, ATR snapshot, TP1/TP2 levels, runner state, and MAE/MFE tracking so analytics match the bot’s database fields.
• Execution hooks: reuse config helpers (quality scoring thresholds, adaptive leverage tiers) to determine whether signals become trades, ensuring apples-to-apples results for v9 vs v10.
• Metrics output: aggregate PnL, win rate, profit factor, MAE/MFE distributions, and per-parameter summaries so sweeps can compare settings quickly.
• Parameter sweeps: now that the simulator is deterministic, the remaining work is orchestration (CLI wrapper + reporting) so we can explore grids quickly.

Data Acquisition

• Use the helper script to pull candles (example grabs ~4 months of SOL/USDT 5m bars):

source .venv/bin/activate
python scripts/export_binance_ohlcv.py \
  --symbol SOLUSDT \
  --interval 5m \
  --start 2025-08-01 \
  --end 2025-11-28 \
  --output data/solusdt_5m.csv

• The script paginates Binance’s REST API (1000-candle chunks), pauses between requests to respect rate limits, and writes timestamped OHLCV columns that backtester.data_loader already expects.
• Re-run the export whenever we want fresh data; adjust --start/--end to widen the window or target different regimes.

CLI Usage

Run a standalone backtest from the project root:

python -m backtester.cli \
  --csv data/solusdt_5m.csv \
  --symbol SOL-PERP \
  --timeframe 5 \
  --start 2025-09-01 \
  --end 2025-09-30 \
  --position-size 2500 \
  --max-bars 300 \
  --export-trades results/sol_sep.csv

The script uses the CSV loader (timestamped OHLCV data), Money Line indicator defaults, and simulator to print summary metrics. Exported CSVs contain per-trade fields (entry/exit, PnL, TP flags, MAE/MFE) for further analysis.

Notes

• Package intentionally avoids dependencies beyond pandas/numpy for portability
• No trading bot files modified; all work lives under backtester/
• Update this document as modules (scoring, simulator, reporting) come online; immediate next edits should cover quality filters + sweep tooling once implemented.