charieswei
/
TradingAgents
mirror of https://github.com/TauricResearch/TradingAgents.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

learn(iterate): 2026-04-12 — surface worst-performing strategies in ranker context; LLM now sees news_catalyst (0% 7d win rate) and social_hype (14.3%) as explicit penalties 

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Youssef Aitousarrah 2026-04-12 18:04:04 -07:00
parent 7ec0e52b98
commit 2d8b91b709
1 changed files with 26 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

34
tradingagents/dataflows/discovery/analytics.py
View File

@ -375,20 +375,38 @@ class DiscoveryAnalytics:
f"Historical 30-day win rate: {overall_30d.get('win_rate', 0)}% ({overall_30d.get('count')} tracked)"
)
# Top performing strategies
# Top and bottom performing strategies
by_strategy = stats.get("by_strategy", {})
if by_strategy:
lines.append("\nBest performing strategies (7-day):")
qualified = [
(k, v)
for k, v in by_strategy.items()
if v.get("win_rate_7d") is not None
and (v.get("wins_7d", 0) + v.get("losses_7d", 0)) >= 5
]
sorted_strats = sorted(
[(k, v) for k, v in by_strategy.items() if v.get("win_rate_7d")],
key=lambda x: x[1].get("win_rate_7d", 0),
reverse=True,
)[:3]
qualified, key=lambda x: x[1].get("win_rate_7d", 0), reverse=True
)
for strategy, data in sorted_strats:
lines.append("\nBest performing strategies (7-day):")
for strategy, data in sorted_strats[:3]:
wr = data.get("win_rate_7d", 0)
avg_ret = data.get("avg_return_7d", 0)
count = data.get("wins_7d", 0) + data.get("losses_7d", 0)
lines.append(f" - {strategy}: {wr}% win rate ({count} samples)")
lines.append(
f" - {strategy}: {wr}% win rate, avg {avg_ret:+.1f}% return ({count} samples)"
)
lines.append(
"\nWORST performing strategies (7-day) — penalize these heavily in scoring:"
)
for strategy, data in sorted_strats[-3:]:
wr = data.get("win_rate_7d", 0)
avg_ret = data.get("avg_return_7d", 0)
count = data.get("wins_7d", 0) + data.get("losses_7d", 0)
lines.append(
f" - {strategy}: {wr}% win rate, avg {avg_ret:+.1f}% return ({count} samples)"
)
return "\n".join(lines) if lines else "No historical data available yet"