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docs: clarify legacy ML target semantics

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Hermes Agent
2026-07-26 23:00:46 +00:00
parent 81654b5743
commit a54dec357f
1 changed files with 7 additions and 12 deletions
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ml_engine/train_and_backtest.py
+7 -12
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@@ -161,8 +161,8 @@ def compute_features(df, config):
def create_accumulation_target(df, config): def create_accumulation_target(df, config):
"""Create accumulation score target based on forward returns. """Create accumulation score target based on forward returns.
For each candle, compute actual forward returns at multiple horizons, For each candle, compute actual forward returns at multiple horizons and
rank them, and create a weighted accumulation score (0-100). map them through fixed, configured return scales to a weighted 0-100 score.
Times when buying led to the best long-term returns get highest scores. Times when buying led to the best long-term returns get highest scores.
""" """
tgt = config.get("target", {}) tgt = config.get("target", {})
@@ -931,10 +931,8 @@ def compile_results(predictions, per_window_cost_improvement,
else: else:
avg_actual_strong = 0.0 avg_actual_strong = 0.0
# We need forward return info. Since actual_score is a rank-based measure (0-100), # The target is a bounded return-quality score, not a realized return.
# and we want to report real forward returns, we approximate: # Report it explicitly as quality rather than approximating a return.
# actual_score > 80 means the buy was in the top 20% of quality.
# For actual forward return stats, we use actual score as a proxy.
# Profitable signals: those where actual score is also above median (50) # Profitable signals: those where actual score is also above median (50)
if strong_buy_count > 0: if strong_buy_count > 0:
@@ -968,14 +966,14 @@ def compile_results(predictions, per_window_cost_improvement,
signal_frequency = strong_buy_count / total_candles * 100 if total_candles > 0 else 0 signal_frequency = strong_buy_count / total_candles * 100 if total_candles > 0 else 0
# --- Score at actual extremes --- # --- Score at actual extremes ---
# "Actual bottoms" = candles with actual score > 85 (top 15% buy opportunities) # "Actual bottoms" = candles with a high realized return-quality score.
actual_bottom_mask = actual_scores > 85 actual_bottom_mask = actual_scores > 85
if np.any(actual_bottom_mask): if np.any(actual_bottom_mask):
avg_score_at_bottoms = float(np.mean(pred_scores[actual_bottom_mask])) avg_score_at_bottoms = float(np.mean(pred_scores[actual_bottom_mask]))
else: else:
avg_score_at_bottoms = 0.0 avg_score_at_bottoms = 0.0
# "Actual tops" = candles with actual score < 15 (worst 15% buy times) # "Actual tops" = candles with a low realized return-quality score.
actual_top_mask = actual_scores < 15 actual_top_mask = actual_scores < 15
if np.any(actual_top_mask): if np.any(actual_top_mask):
avg_score_at_tops = float(np.mean(pred_scores[actual_top_mask])) avg_score_at_tops = float(np.mean(pred_scores[actual_top_mask]))
@@ -1000,10 +998,7 @@ def compile_results(predictions, per_window_cost_improvement,
count = int(np.sum((pred_scores >= lo) & (pred_scores < (hi if hi < 100 else 101)))) count = int(np.sum((pred_scores >= lo) & (pred_scores < (hi if hi < 100 else 101))))
score_distribution[key] = count score_distribution[key] = count
# --- Forward return approximation from actual scores --- # --- Realized return-quality summary ---
# Map actual score to approximate return quality
# Score 90+ = historically best 10% buys, score 10- = worst 10%
# Use actual score as proxy for "quality rank"
if strong_buy_count > 0: if strong_buy_count > 0:
# Average actual quality score for strong buy signals # Average actual quality score for strong buy signals
avg_quality_strong = float(np.mean(actual_scores[strong_buy_mask])) avg_quality_strong = float(np.mean(actual_scores[strong_buy_mask]))