Files

21in7 771b357f28 feat: enhance strategy parameter sweep with new CLI tool and bug fixes

- Added a new CLI tool `scripts/strategy_sweep.py` for executing parameter sweeps.
- Updated `get_signal()` and `_calc_signals()` methods to accept `signal_threshold`, `adx_threshold`, and `volume_multiplier` parameters for improved signal processing.
- Fixed a bug in `WalkForwardBacktester` that prevented proper propagation of signal parameters, ensuring accurate backtesting results.
- Updated documentation to reflect changes in parameter sweeps and results.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-07 00:27:28 +09:00

4.5 KiB

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Strategy Parameter Sweep Plan

Date: 2026-03-06 Status: Completed

Goal

Find profitable parameter combinations for the base technical indicator strategy (ML OFF) using walk-forward backtesting, targeting PF >= 1.0 as foundation for ML redesign.

Background

Walk-forward backtest revealed the current XRP strategy is unprofitable (PF 0.71, -641 PnL). The strategy parameter sweep systematically tests 324 combinations of 5 parameters to find profitable regimes.

Parameters Swept

Parameter	Values	Description
`atr_sl_mult`	1.0, 1.5, 2.0	Stop-loss ATR multiplier
`atr_tp_mult`	2.0, 3.0, 4.0	Take-profit ATR multiplier
`signal_threshold`	3, 4, 5	Min weighted indicator score for entry
`adx_threshold`	0, 20, 25, 30	ADX filter (0=disabled, N=require ADX>=N)
`volume_multiplier`	1.5, 2.0, 2.5	Volume surge detection multiplier

Total combinations: 3 x 3 x 3 x 4 x 3 = 324

Implementation

Files Modified

src/indicators.py — get_signal() accepts signal_threshold, adx_threshold, volume_multiplier params
src/dataset_builder.py — _calc_signals() accepts same params for vectorized computation
src/backtester.py — BacktestConfig includes strategy params; WalkForwardBacktester propagates them to test folds

Files Created

scripts/strategy_sweep.py — CLI tool for parameter grid sweep

Bug Fix

WalkForwardBacktester was not passing signal_threshold, adx_threshold, volume_multiplier, or use_ml to fold BacktestConfig. All signal params were silently using defaults, making ADX/volume/threshold sweeps have zero effect.

Results (XRPUSDT, Walk-Forward 3/1)

Top 10 Combinations

Rank	SL×ATR	TP×ATR	Signal	ADX	Vol	Trades	WinRate	PF	MDD	PnL	Sharpe
1	1.5	4.0	3	30	2.5	19	52.6%	2.39	7.0%	+469	61.0
2	1.5	2.0	3	30	2.5	19	68.4%	2.23	6.5%	+282	61.2
3	1.0	2.0	3	30	2.5	19	57.9%	1.98	5.0%	+213	50.8
4	1.0	4.0	3	30	2.5	19	36.8%	1.80	7.7%	+248	37.1
5	1.5	3.0	3	30	2.5	19	52.6%	1.76	10.1%	+258	40.9
6	1.5	4.0	3	25	2.5	28	42.9%	1.75	13.1%	+381	36.8
7	2.0	4.0	3	30	1.5	39	48.7%	1.67	16.9%	+572	35.3
8	1.0	2.0	3	25	2.5	28	50.0%	1.64	5.8%	+205	35.7
9	1.5	2.0	3	25	2.5	28	57.1%	1.62	10.3%	+229	35.7
10	2.0	2.0	3	25	2.5	27	66.7%	1.57	12.0%	+217	33.3

Current Production (Rank 93/324)

SL×ATR	TP×ATR	Signal	ADX	Vol	Trades	WinRate	PF	MDD	PnL
1.5	3.0	3	0	1.5	118	30.5%	0.71	65.9%	-641

Key Findings

ADX filter is the single most impactful parameter. All top 10 results use ADX >= 25, with ADX=30 dominating the top 5. This filters out sideways/ranging markets where signals are noise.
Volume multiplier 2.5 dominates. Higher volume thresholds ensure entries only on strong conviction (genuine breakouts vs. noise).
Signal threshold 3 is optimal. Higher thresholds (4, 5) produced too few trades or zero trades in most ADX-filtered regimes.
SL/TP ratios matter less than entry filters. The top results span all SL/TP combos, but all share ADX=25-30 + Vol=2.5.
Trade count drops significantly with filters. Top combos have 19-39 trades vs. 118 for current. Fewer but higher quality entries.
41 combinations achieved PF >= 1.0 out of 324 total (12.7%).

Recommended Next Steps

Update production defaults: ADX=25, volume_multiplier=2.0 as a conservative choice (more trades than ADX=30)
Validate on TRXUSDT and DOGEUSDT to confirm ADX filter is not XRP-specific
Retrain ML models with updated strategy params — the ML filter should now have a profitable base to improve upon
Fine-tune sweep around the profitable zone: ADX [25-35], Vol [2.0-3.0]

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