Improve Trader entry quality training diagnostics
This commit is contained in:
Codex
@@ -0,0 +1,173 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
TRAINING_ROOT = Path(__file__).resolve().parents[1]
|
||||
if str(TRAINING_ROOT) not in sys.path:
|
||||
sys.path.insert(0, str(TRAINING_ROOT))
|
||||
|
||||
from trader_training.labels import DEFAULT_LABEL_CONFIG, _path_stats_for_group
|
||||
from trader_training.pm import _probability_implied_edge, _simulate_open_trades, _threshold_candidates, default_pm_config
|
||||
|
||||
|
||||
class RiskPmFixTest(unittest.TestCase):
|
||||
def test_path_stats_never_writes_negative_adverse_or_favorable_move(self) -> None:
|
||||
frame = pd.DataFrame(
|
||||
{
|
||||
"event_time": pd.date_range("2026-01-01", periods=4, freq="min", tz="UTC"),
|
||||
"open_time_ms": np.arange(4, dtype=np.int64) * 60_000,
|
||||
"symbol": "BTC-USDT-PERP",
|
||||
"close": [100.0, 101.0, 102.0, 103.0],
|
||||
"high": [100.0, 101.0, 102.0, 103.0],
|
||||
"low": [100.0, 101.0, 102.0, 103.0],
|
||||
"spread_bps": [1.0, 1.0, 1.0, 1.0],
|
||||
}
|
||||
)
|
||||
|
||||
long_stats = _path_stats_for_group(frame, "LONG", horizon=2, target_bps=500.0, stop_bps=500.0)
|
||||
short_stats = _path_stats_for_group(frame, "SHORT", horizon=2, target_bps=500.0, stop_bps=500.0)
|
||||
|
||||
self.assertGreaterEqual(float(long_stats["mae_bps"].min()), 0.0)
|
||||
self.assertGreaterEqual(float(long_stats["mfe_bps"].min()), 0.0)
|
||||
self.assertGreaterEqual(float(short_stats["mae_bps"].min()), 0.0)
|
||||
self.assertGreaterEqual(float(short_stats["mfe_bps"].min()), 0.0)
|
||||
|
||||
def test_default_risk_labels_match_design_thresholds(self) -> None:
|
||||
self.assertEqual(45, DEFAULT_LABEL_CONFIG["continue"]["horizon_minutes"])
|
||||
self.assertEqual(60.0, DEFAULT_LABEL_CONFIG["risk"]["market_drawdown_bps"])
|
||||
self.assertEqual(35.0, DEFAULT_LABEL_CONFIG["risk"]["position_path_risk_bps"])
|
||||
self.assertEqual(80.0, DEFAULT_LABEL_CONFIG["risk"]["spike_bps"])
|
||||
self.assertEqual(1.8, DEFAULT_LABEL_CONFIG["risk"]["vol_expansion_ratio"])
|
||||
|
||||
def test_pm_search_covers_low_entry_probability_without_allowing_negative_edge(self) -> None:
|
||||
candidates = _threshold_candidates()
|
||||
|
||||
self.assertTrue(candidates)
|
||||
self.assertLessEqual(max(item["max_market_risk_prob"] for item in candidates), 0.98)
|
||||
self.assertLessEqual(min(item["min_entry_prob"] for item in candidates), 0.03)
|
||||
self.assertGreaterEqual(min(item["min_expected_edge_bps"] for item in candidates), 0.0)
|
||||
|
||||
def test_probability_implied_edge_uses_price_plan_payoff(self) -> None:
|
||||
edge = _probability_implied_edge(
|
||||
pd.Series([0.10, 0.50]),
|
||||
{"targetDistanceBps": 120.0, "stopDistanceBps": 2.0, "costBps": 6.5},
|
||||
)
|
||||
|
||||
self.assertAlmostEqual(3.7, float(edge.iloc[0]), places=6)
|
||||
self.assertAlmostEqual(52.5, float(edge.iloc[1]), places=6)
|
||||
|
||||
def test_pm_backtest_sizing_uses_position_manager_formula_not_fixed_floor(self) -> None:
|
||||
frame = pd.DataFrame(
|
||||
{
|
||||
"sample_id": ["s0"],
|
||||
"symbol": ["BTC-USDT-PERP"],
|
||||
"event_time": pd.to_datetime(["2026-01-01T00:00:00Z"]),
|
||||
"split_id": ["tune_inner"],
|
||||
"long_prob": [0.70],
|
||||
"short_prob": [0.10],
|
||||
"neutral_prob": [0.20],
|
||||
"long_entry_prob": [0.80],
|
||||
"short_entry_prob": [0.20],
|
||||
"market_risk_prob": [0.20],
|
||||
"long_position_risk_prob": [0.10],
|
||||
"short_position_risk_prob": [0.10],
|
||||
"pred_long_expected_net_edge_bps": [40.0],
|
||||
"pred_short_expected_net_edge_bps": [1.0],
|
||||
"actual_long_expected_net_edge_bps": [30.0],
|
||||
"actual_short_expected_net_edge_bps": [-10.0],
|
||||
"long_trade_net_edge_bps": [11.0],
|
||||
"short_trade_net_edge_bps": [-14.5],
|
||||
"long_target_hit": [1],
|
||||
"short_target_hit": [0],
|
||||
"long_stop_hit": [0],
|
||||
"short_stop_hit": [1],
|
||||
"long_time_to_target_ms": [300_000],
|
||||
"short_time_to_target_ms": [-1],
|
||||
"long_time_to_stop_ms": [-1],
|
||||
"short_time_to_stop_ms": [180_000],
|
||||
"long_entry_target": [1],
|
||||
"short_entry_target": [0],
|
||||
}
|
||||
)
|
||||
thresholds = {
|
||||
"long_open_prob": 0.55,
|
||||
"short_open_prob": 0.55,
|
||||
"min_entry_prob": 0.55,
|
||||
"max_market_risk_prob": 0.55,
|
||||
"min_expected_edge_bps": 3.0,
|
||||
"min_direction_margin": 0.02,
|
||||
}
|
||||
|
||||
trades = _simulate_open_trades(
|
||||
frame,
|
||||
thresholds,
|
||||
default_pm_config(),
|
||||
{"stopDistanceBps": 8.0, "costBps": 6.5},
|
||||
)
|
||||
|
||||
self.assertEqual(1, len(trades))
|
||||
self.assertAlmostEqual(11.0, float(trades.iloc[0]["actual_edge_bps"]))
|
||||
self.assertAlmostEqual(30.0, float(trades.iloc[0]["label_max_edge_bps"]))
|
||||
self.assertGreater(float(trades.iloc[0]["planned_ratio"]), 0.05)
|
||||
self.assertLessEqual(float(trades.iloc[0]["planned_ratio"]), 0.20)
|
||||
|
||||
def test_pm_backtest_blocks_overlapping_open_trades_until_exit_and_cooldown(self) -> None:
|
||||
frame = pd.DataFrame(
|
||||
{
|
||||
"sample_id": ["s0", "s1"],
|
||||
"symbol": ["BTC-USDT-PERP", "BTC-USDT-PERP"],
|
||||
"event_time": pd.to_datetime(["2026-01-01T00:00:00Z", "2026-01-01T00:01:00Z"]),
|
||||
"split_id": ["tune_inner", "tune_inner"],
|
||||
"long_prob": [0.70, 0.72],
|
||||
"short_prob": [0.10, 0.10],
|
||||
"neutral_prob": [0.20, 0.18],
|
||||
"long_entry_prob": [0.80, 0.82],
|
||||
"short_entry_prob": [0.20, 0.20],
|
||||
"market_risk_prob": [0.20, 0.20],
|
||||
"long_position_risk_prob": [0.10, 0.10],
|
||||
"short_position_risk_prob": [0.10, 0.10],
|
||||
"pred_long_expected_net_edge_bps": [40.0, 42.0],
|
||||
"pred_short_expected_net_edge_bps": [1.0, 1.0],
|
||||
"actual_long_expected_net_edge_bps": [30.0, 31.0],
|
||||
"actual_short_expected_net_edge_bps": [-10.0, -10.0],
|
||||
"long_trade_net_edge_bps": [11.0, 12.0],
|
||||
"short_trade_net_edge_bps": [-14.5, -14.5],
|
||||
"long_target_hit": [1, 1],
|
||||
"short_target_hit": [0, 0],
|
||||
"long_stop_hit": [0, 0],
|
||||
"short_stop_hit": [1, 1],
|
||||
"long_time_to_target_ms": [300_000, 300_000],
|
||||
"short_time_to_target_ms": [-1, -1],
|
||||
"long_time_to_stop_ms": [-1, -1],
|
||||
"short_time_to_stop_ms": [180_000, 180_000],
|
||||
"long_entry_target": [1, 1],
|
||||
"short_entry_target": [0, 0],
|
||||
}
|
||||
)
|
||||
thresholds = {
|
||||
"long_open_prob": 0.55,
|
||||
"short_open_prob": 0.55,
|
||||
"min_entry_prob": 0.55,
|
||||
"max_market_risk_prob": 0.55,
|
||||
"min_expected_edge_bps": 3.0,
|
||||
"min_direction_margin": 0.02,
|
||||
}
|
||||
|
||||
trades = _simulate_open_trades(
|
||||
frame,
|
||||
thresholds,
|
||||
default_pm_config(),
|
||||
{"stopDistanceBps": 8.0, "costBps": 6.5, "maxHoldMinutes": 45},
|
||||
)
|
||||
|
||||
self.assertEqual(1, len(trades))
|
||||
self.assertEqual("s0", trades.iloc[0]["sample_id"])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user