Trading Wisdom

Last updated: 2026-01-17 20:31 UTC Active patterns: 206 Total samples: 41088 Confidence threshold: 60%

Key Learnings

CRITICAL: In moderate bull markets (4/5 assets positive), ALL active trading strategies lost money while zero-trade strategies preserved capital perfectly.
Trade frequency is inversely correlated with performance in this regime: 0 trades = $0 loss, 23 trades = -$28.69, 243 trades = -$229.00.
Technical analysis signals (multi-timeframe alignment, MACD, RSI, SMA) failed to predict direction for both long and short entries in this moderate bull environment.
Asset selection mattered significantly: BNB (+2.03%) vs SOL (-0.09%). Agents fixating on SOL 'uptrend' (llama4_scout) suffered worst losses.
Validation frameworks and risk management rules do not prevent losses when the fundamental market direction assessment is wrong.
High-confidence decisions (0.85-0.90) on directional trades were frequently wrong, suggesting confidence calibration issues across all active agents.
The only reliable pattern was proactive loss-cutting with high confidence (0.85-0.95) to limit drawdown.

Confidence: 95%
Total samples: 4
Times confirmed: 1
First seen: 2026-01-17
Details: Zero-trade strategy in moderate bull markets preserves capital perfectly. Agents that made 0 trades (learning_qwen, gpt_simple, qwen3_235b, index_fund) achieved $0.00 PnL while all active traders lost money despite BNB +2.03%, ETH +1.02%, DOGE +1.07% gains.

Confidence: 92%
Total samples: 771
Times confirmed: 1
First seen: 2026-01-17
Details: Zero-trade strategies preserve capital in mixed/choppy markets. learning_qwen, gpt_simple, and index_fund made 0 trades and achieved $0.00 PnL, outperforming all active traders in this low-conviction environment.

Confidence: 92%
Total samples: 4
Times confirmed: 1
First seen: 2026-01-17
Details: Zero-trade strategy preserves capital in moderately bullish markets where active trading leads to losses. Agents holding no positions avoided the -$50 to -$264 losses seen by active traders despite market being up +0.63% to +2.15%.

Confidence: 90%
Total samples: 368
Times confirmed: 1
First seen: 2026-01-17
Details: Close losing positions proactively with high confidence (0.8-0.9) to free margin and limit drawdowns. Multiple agents demonstrated this: gptoss_20b_simple closing SOL at -$4.76 loss, agentic_gptoss closing DOGE 'largest loss percentage'.

Confidence: 88%
Total samples: 257
Times confirmed: 1
First seen: 2026-01-17
Details: Minimal trading with high selectivity outperforms frequent trading. qwen3_235b made only 2 trades with PnL of -$0.29, dramatically outperforming agents with 140-201 trades.

Confidence: 88%
Total samples: 89
Times confirmed: 1
First seen: 2026-01-17
Details: Closing long positions with high confidence (0.92) when regime shifts to 'moderate bearish' preserves capital. skill_only_oss reasoning: 'risk-management rules advise limiting exposure and closing long positions to preserve capital'.

Confidence: 88%
Total samples: 1
Times confirmed: 1
First seen: 2026-01-17
Details: Minimal trading frequency (23 trades) with technical analysis baseline outperforms high-frequency approaches. ta_baseline lost only $-28.69 vs llama4_scout's $-229.00 with 243 trades.

Confidence: 85%
Total samples: 160
Times confirmed: 1
First seen: 2026-01-17
Details: Explicit risk validation with 2% equity risk and 2:1 reward ratio combined with position closing discipline. skill_only_oss achieved best active trader performance (-$17.96) with 160 trades, using validated risk parameters.

Confidence: 85%
Total samples: 100
Times confirmed: 1
First seen: 2026-01-16
Details: Agentic approach with active position management: opening shorts in bearish markets, closing positions to lock gains when technical indicators confirm trend reversal. Uses SMA crossover + MACD + Bollinger bands for entry/exit confirmation with explicit validation steps.

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