CZ is not the only one wondering how @the_nof1 really works - plenty of detailed performance and trade analyses (h/t @jinglingcookies @jay_azhang) but haven't seen anyone looking at the actual prompts yet. here's what the models get:
> asset universe: BTC, ETH, SOL, BNB, DOGE, XRP
> instruments: Hyperliquid perps only with leverage 5-40x
> every 2min: timestamp + portfolio + coin data
>> portfolio: session tracking (invocations, time), positions and performance (entry/exit prices, leverage, returns, sharpe ratios, cash left)
>> coin data: price, OI, volume, funding rates, EMA, MACD, RSI, ATR
> system guardrails
>> G0: allowed model actions are 'buy_to_enter', 'sell_to_enter', 'hold', 'close_position'
>> G1: "No pyramiding or size increases" cannot add to existing positions, only enter new ones
>> G2: has to output JSON for each position with quantity, profit_target, stop_loss, invalidation_condition, leverage, confidence, risk_usd (see image)
>> G3: entry, exit and close actions need extra "justification" field in the JSON with reasoning
overall it's a well-scoped experiment but the decision space for the models is relatively narrow for now. looking forward to see where they take it from here!


Saw this a lot in my feed.
DeepSeek out performing the rest in AI trading. How does this work?
I thought trading strategies work best if you have your own unique strategy that is better than others, AND no one else has it. Otherwise, you are just buying and selling at the same time as others.
A counter argument could be made that enough people use the same AI, then its buying power will push price up by itself, and vice versa. 🤔
There will probably be a lot of people researching AI for trading after this. Expect more trading volumes.
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