lowkey_backtest

Loads 1-minute BTC/USD data from ../data/btcusd_1-min_data.csv
Aggregates to multiple timeframes (e.g., 5min, 15min, 30min, 1h, 4h, 1d)
Computes three Supertrend variants and creates a meta signal when all agree
Executes entries/exits at the aggregated bar open price
Applies OKX spot fee assumptions (taker by default)
Evaluates stop-loss using intra-bar 1-minute data
Writes detailed trade logs and a summary CSV

Requirements

Python 3.12+
Dependencies: pandas, numpy, ta
Package management: uv

Install dependencies with uv:

uv sync
# If a dependency is missing, add it explicitly and sync
uv add pandas numpy ta
uv sync

Data

Expected CSV location: ../data/btcusd_1-min_data.csv (relative to the repo root)
Required columns: Timestamp, Open, High, Low, Close, Volume
Timestamp should be UNIX seconds; zero-volume rows are ignored

Quickstart

Run the backtest with defaults:

uv run python main.py

Outputs:

Per-run trade logs in backtest_logs/ named like trade_log_<TIMEFRAME>_sl<STOPLOSS>.csv
Run-level summary in backtest_summary.csv

Configuring a Run

Adjust parameters directly in main.py:

Date range (in load_data): load_data('2021-11-01', '2024-10-16')
Timeframes to test (any subset of "5min", "15min", "30min", "1h", "4h", "1d"):
- timeframes = ["5min", "15min", "30min", "1h", "4h", "1d"]
Stop-loss percentages:
- stoplosses = [0.03, 0.05, 0.1]
Supertrend settings (in add_supertrend_indicators): (period, multiplier) pairs (12, 3.0), (10, 1.0), (11, 2.0)
Fee model (in calculate_okx_taker_maker_fee): taker 0.0010, maker 0.0008

What the Backtester Does

Aggregation: Resamples 1-minute data to your selected timeframe using OHLCV rules
Supertrend signals: Computes three Supertrends and derives a meta trend of +1 (bullish) or -1 (bearish) when all agree; otherwise 0
Trade logic (long-only):
- Entry when the meta trend changes to bullish; uses aggregated bar open price
- Exit when the meta trend changes to bearish; uses aggregated bar open price
- Stop-loss: For each aggregated bar, scans corresponding 1-minute closes to detect stop-loss and exits using a realistic fill (threshold or next 1-minute open if gapped)
Performance metrics: total return, max drawdown, Sharpe (daily, factor 252), win rate, number of trades, final/initial equity, and total fees

Important: Lookahead Bias Note

The current implementation uses the meta Supertrend signal of the same bar for entries/exits, which introduces lookahead bias. To avoid this, lag the signal by one bar inside backtest() in main.py:

# Replace the current line
meta_trend_signal = meta_trend

# With a one-bar lag to remove lookahead
# meta_trend_signal = np.roll(meta_trend, 1)
# meta_trend_signal[0] = 0

Outputs

backtest_logs/trade_log_<TIMEFRAME>_sl<STOPLOSS>.csv: trade-by-trade records including type (buy, sell, stop_loss, forced_close), timestamps, prices, balances, PnL, and fees
backtest_summary.csv: one row per (timeframe, stop-loss) combination with timeframe, stop_loss, total_return, max_drawdown, sharpe_ratio, win_rate, num_trades, final_equity, initial_equity, num_stop_losses, total_fees

Troubleshooting

CSV not found: Ensure the dataset is located at ../data/btcusd_1-min_data.csv
Missing packages: Run uv add pandas numpy ta then uv sync
Memory/performance: Large date ranges on 1-minute data can be heavy; narrow the date span or test fewer timeframes