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fix: adaptive exploration boost when SOTA dominated by single indicator

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- +25% explore when >80% SOTA shares same indicator
- Force non-dominant indicator every 100 iterations
- Base exploration raised to 40% (effective 30% with 20 SOTA)
This commit is contained in:
TPTBusiness
2026-05-31 17:43:45 +02:00
parent 6bce4f2405
commit b793a8114b
1 changed files with 34 additions and 2 deletions
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scripts/nexquant_rd_loop.py
+34 -2
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@@ -78,7 +78,7 @@ STRATEGY_TYPES = ["single", "multi_tf", "portfolio", "multi_role"]
TREND_TFS = ["30min", "1h", "4h"] # higher TFs for trend filter
ENTRY_TFS = ["5min", "15min", "30min"] # lower TFs for entry
MIN_SHARPE, MIN_TRADES = 0.5, 20
EXPLORATION_RATE = 0.30 # 30% explore, 70% exploit
EXPLORATION_RATE = 0.40 # 40% explore, 60% exploit
# ═══════════════════════════════════════════════════════════════════════════════
# Evaluation
@@ -256,7 +256,39 @@ class ResearchLoop:
'description': f"ML: LightGBM on {len(self.sota)} strategies",
'sota': self.sota[:5]}
if random.random() < self.exploration_rate or not self.sota:
# Every 100 iterations: force non-dominant indicator exploration
if self.iteration % 100 == 0 and len(self.sota) >= 5:
top_ind = self.sota[0]['hypothesis'].get('trend_ind', self.sota[0]['hypothesis'].get('indicator'))
hp = self._random_hypothesis()
# Ensure at least one role uses a different indicator
if hp.get('type') == 'multi_role' and hp['trend_ind'] == top_ind and hp['entry_ind'] == top_ind:
if random.random() < 0.5:
hp['trend_ind'] = random.choice([i for i in INDICATORS_POOL if i != top_ind])
hp['trend_params'] = self._random_params(hp['trend_ind'])
else:
hp['entry_ind'] = random.choice([i for i in INDICATORS_POOL if i != top_ind])
hp['entry_params'] = self._random_params(hp['entry_ind'])
hp['description'] = f"{hp['trend_ind']}({hp['trend_tf']})→{hp['entry_ind']}({hp['entry_tf']})"
elif hp.get('type') == 'single' and hp.get('indicator') == top_ind:
hp['indicator'] = random.choice([i for i in INDICATORS_POOL if i != top_ind])
hp['params'] = self._random_params(hp['indicator'])
hp['description'] = f"{hp['indicator']} on {hp['timeframe']}"
elif hp.get('type') == 'multi_tf' and hp.get('indicator') == top_ind:
hp['indicator'] = random.choice([i for i in INDICATORS_POOL if i != top_ind])
hp['params'] = self._random_params(hp['indicator'])
hp['description'] = f"{hp['indicator']} on {','.join(hp['timeframes'][:2])}"
hp['generation'] = 'explore'
return hp
# Boost exploration when SOTA is dominated by one indicator
effective_rate = self.exploration_rate
if len(self.sota) >= 10:
top_ind = self.sota[0]['hypothesis'].get('trend_ind', self.sota[0]['hypothesis'].get('indicator'))
dominated = sum(1 for r in self.sota if r['hypothesis'].get('trend_ind', r['hypothesis'].get('indicator')) == top_ind)
if dominated > len(self.sota) * 0.8: # >80% same indicator
effective_rate += 0.25 # +25% explore boost
if random.random() < effective_rate or not self.sota:
return self._random_hypothesis()
else:
base = random.choice(self.sota[:5])