A $93B HEDGE FUND. TWO INPUTS. THREE OUTPUTS. Coatue's Head of AI described a hedge fund as a function. External context + internal context go in. What to buy, how much and when come out. I turned that mental model into a build prompt. Here is the architecture: 1. INGEST News, prices and filings through APIs. Everything an investor sees on a work screen should be available to an agent. 2. REMEMBER Positions, liquidity, risk requirements and previous investment cases. Coatue's advantage includes decades of documented internal context. 3. MAP Break the decision into nodes. Define each input, output and branch. One step's output becomes the next step's input. 4. ROUTE Code for explicit rules. ML for statistical relationships. LLMs for complex text. People for tasks that require human interaction and judgment. 5. EVALUATE Give each node a measurable quality target. Without a target, changing models and prompts is just turning knobs. 6. DECIDE Compress the evidence into selection, size and timing. Keep the reasoning traceable to the inputs. The goal is to maximize decision quality at every step. Your agent can read today's news. Can it remember why yesterday's investment thesis failed? Save the prompt before building your next trading agent.

wall street spent 30 years building trading systems that retail traders were never supposed to compete with that advantage is starting to disappear S&P 500 futures. AI bias at 59 - bullish sellers aggressively hitting the bid. price refusing to move lower CVD divergence. seller absorption. VWAP confluence then aggressive buyers step in a +4R trading opportunity emerges from something most retail traders would have completely missed the signal wasn't a magical AI prediction it was the combination of market structure, real order flow, and machine-generated context jane street. citadel. two sigma. hudson river trading these firms built billion-dollar operations around one fundamental advantage turning massive amounts of market data into systematic, testable decisions not guessing. not chasing indicators. not asking an AI chatbot which stock will explode tomorrow data. hypotheses. backtesting. statistical validation. risk management. execution for decades, building that kind of infrastructure required millions in capital, advanced mathematics, and teams of quantitative researchers that barrier is collapsing Claude can now connect to quantitative research infrastructure through QuantGPT historical market data. strategy research. backtesting. performance comparisons. validation workflows QuantGPT Pro adds cloud execution for user-written strategies connected to Alpaca what once required multiple specialized tools and serious engineering expertise is becoming accessible through an AI interface and that's where the real disruption begins AI can help identify a market hypothesis quantitative infrastructure can test whether the hypothesis survives historical data order flow can provide context for what buyers and sellers are actually doing execution systems can turn validated rules into monitored trading operations none of this guarantees a profitable strategy but it fundamentally changes who gets to investigate one the average retail trader is still collecting indicators and searching for the perfect entry the next generation is learning to build research systems that can reject bad strategies before real money is involved the biggest shift in trading isn't that AI can predict the market it's that the tools to research, test, and execute systematic strategies are moving beyond institutional trading desks wall street spent decades making quantitative infrastructure an exclusive advantage ai is making access to that infrastructure a completely different game most traders are still looking for the next signal the real opportunity is learning how to build the system behind it

i nearly lost my mind when i realized how simple this setup was. AI can test 100 variations and hand you the luckiest backtest as "alpha." this prompt puts Claude Code, Grok Bot and Jev on that exact problem. > Claude Code builds the desk. > Grok Bot keeps research running 24/7. > Python attacks the backtest with fees and unseen data. > Jev checks live conditions before each candidate entry. failed checks block the trade. code controls the orders and risk. paste this into Claude Code with your strategy, exchange and risk limits. start with the backtest you trust the most.

this is f$$king insane the trader goes to sleep. the trading desk keeps working. here's how to build a 24/7 Jev setup for block-speed crypto trading. the goal is simple - read the order book as new blocks land filter dangerous conditions before taking risk let hard rules control every order Jev's published response time is 70–500 milliseconds. narrow questions in, structured answers out. no paragraph standing between the market and your execution code. here is how to build the setup: 1. open claude code and scaffold an async Python worker. run "pip install typesafe-sdk", configure your API key, pin "jev-1.13.0" and start in paper mode. 2. connect one market: MON/USDC on Kuru. stream new Monad blocks, read the book and recent trades, timestamp every snapshot. keep one model request in flight and process the newest state. 3. calculate spread, depth imbalance, aggressive buying versus selling, short-term volatility and inventory in code. give Jev those numbers with recent history. don’t pay AI to do arithmetic. 4. ask six independent questions in one call: market regime, direction over the next 10 blocks, flow toxicity, liquidity stress, setup quality and pressure to reduce exposure. define the answer options and scoring criteria explicitly. 5. code applies the trading rule: quote toward the directional bias only when quality clears your tested threshold and the flow gates pass. use post-only limits, reject stale answers and enforce position, order-size and daily-loss caps. risk controls keep running when Jev doesn’t. 6. replay recorded order books on an untouched period. include fees, gas, execution delays and conservative fill assumptions. then paper trade continuously, logging rejected setups, fills, drawdown and whether the model’s probabilities match outcomes. $0.042 per million input tokens. output is free. the expensive part is finding out your beautiful backtest assumed fills you would never get. let Jev judge the conditions. let code price the order. let the kill switch have the final word.

this feels like a f*cking cheat code a trading bot can cite 10 sources and still have zero evidence for the trade RAG finds the closest passages. the closest passage can still be last quarter's guidance or the sixth copy of the same headline feed that straight to the LLM and a search match can become your reason to buy you just turned similarity into conviction put Jev between retrieval and generation does this passage actually support the claim? does it contradict the setup? is it only talking about the same ticker? Jev returns structured judgments. code keeps useful evidence, preserves contradictions, and drops the noise a second check asks whether the surviving context is enough to answer if it isn't: more research or WAIT 3 repos to build the stack: -> FinMem: layered memory for an LLM trading agent github.com/pipiku915/FinMem-… -> jev-reranker: Jev reranking and evidence filtering before generation github.com/hotchpotch/jev-re… -> QuantDinger: paper/live trading with a Jev decision gate before entry orders github.com/OpenByteInc/Quant… $0.042 per million input tokens. output is free 10,000 total input tokens across the Jev checks = $0.00042 data, embeddings and the writing model have their own bill the catch: an evidence score is not a win rate. keep the rejected candidates and paper trade the full loop. configure failed checks to block new entries while keeping exits available "let RAG find the context. let Jev check the evidence. let code decide whether an order is allowed." bookmark this before your bot mistakes a citation for an edge

jev is making the calls on polymarket for fractions of a cent. your next trading rival might not be human this one spends the round saying no jev returns a decision. the software checks it against the entry threshold the call shown in the log: 485ms. $0.000250 the output stays below the threshold. the bot holds flat while bitcoin moves and the clock runs down no trade is still a decision. it just doesn't give you a position to brag about that's where jev fits: small judgments the software can use directly, without generating another explanation at every fork the model judges. code enforces the rules. the log shows what happened before asking whether your ai can find a winning bet, ask whether it can leave a market alone bookmark it

we built a local Jev-style AI trader. its most important result was an order that never got sent. Cloudflare's Jev-compatible Clef had three choices: NORMAL, WIDE, PAUSE. No BUY button. No permission to change position limits. Code owned the order book, quotes, cancels and fills. We ran a stress replay. As order flow turned one-sided, the bot moved from NORMAL to WIDE and pulled its quotes back. When the pressure persisted, it moved to PAUSE and stopped sending new orders. Then came the result that mattered more than the mode switch: an unsafe book stopped new orders in code, without waiting for Clef's next answer. The model could change how the bot quoted. It could not talk the bot past a hard stop. The log showed the chain from market snapshot to model choice to quote action to risk veto. It also kept compute and trading costs beside those decisions. No green P&L screenshot. The result was a bot that could refuse its own model before an order reached the exchange. save this, then open the piece below

the trader everyone follows is trying to call the next move the builder nobody follows is automating the decisions between the chart and the order both are looking at order flow. one needs to stay at the screen. the other is turning parts of that work into software that's the opportunity hiding behind jev most conversations about ai trading start with the same question: can it find me a winning trade? a more useful starting point is the decision you keep making by hand does this sequence match the setup? has the evidence changed? continue, wait, or send it for review? those questions can become a repeatable loop our stack gives each part a specific job the llm researches and develops the trading thesis jev reads the current state and chooses from answers we defined in advance, with probabilities attached code calculates exposure, enforces the limits and handles permitted orders. exchange records confirm what actually filled unclear evidence goes to review. hard limits run independently of the model now each judgment has an input you can replay, an answer you can inspect, and an outcome you can check that's why jev belongs here: the software needs a decision it can branch on, and repeatedly generating an explanation adds work to that path start with one recurring judgment. define its possible answers. test the loop in paper mode save this before your next session the opportunity is hidden in the part of trading you still repeat by hand

A developer finds a trading bot on GitHub on Friday night. He pairs CloddsBot with an OpenAI Dot, gives the bot a test account, and goes to sleep. By morning, the account is up $2,000. The interesting part is how the two agents divided the work. CloddsBot scanned Polymarket, Binance, and other markets for opportunities. Its trading and risk systems handled the orders. Before the run, the Dot checked the setup and risk limits. Overnight, it reviewed market data, signals, filled orders, and risk events through a protected read-only connection. Its cloud computer kept working after the developer closed his laptop. In the morning, he opened more than a P&L figure. He could trace what the bot found, what it tried to trade, and what actually filled. A few years ago, that workflow would have looked like a small trading desk. Here, it started with a public GitHub repo, a personal cloud agent, and one developer making the final call. Repo: github.com/alsk1992/CloddsBo…