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Anya Rossi

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M1 - you read news. You trade on gut feeling. Win rate 44%. Slow bleed. M2 - three formulas. EV. Kelly. Base rate. Win rate 58%. You stop losing. M3 - Random Forest. 100 trees. 38 features. Win rate 72%. First real edge. M4 - 10,000 simulations. CI [87.3%, 91.8%]. Not luck. Proven edge. M5 - XGBoost. 330 trees. Loss 0.891 → 0.094. Win rate 89.6%. Verified on 1,870 real trades. M6 - RL agent. 50,000 iterations. Trades 24/7. Learns from every move. You're no longer in the equation...

M1 - you read news. You trade on gut feeling. Win rate 44%. Slow bleed. M2 - three formulas. EV. Kelly. Base rate. Win rate 58%. You stop losing. M3 - Random Forest. 100 trees. 38 features. Win rate 72%. First real edge. M4 - 10,000 simulations. CI [87.3%, 91.8%]. Not luck. Proven edge. M5 - XGBoost. 330 trees. Loss 0.891 → 0.094. Win rate 89.6%. Verified on 1,870 real trades. M6 - RL agent. 50,000 iterations. Trades 24/7. Learns from every move. You're no longer in the equation...

140,252 Aufrufe • vor 5 Monaten

2026 might be the year where hedge fund level research becomes accessible to literally anyone. Some random guy with Claude, a few regulatory PDFs and $12 of API credits can now do the kind of reverse engineering that used to require an actual team of analysts. Meanwhile most people still think AI is for anime pictures. This Jane Street / SEBI breakdown is one of the first articles in a while that actually feels like a glimpse of where things are going More like, AI turning highly asymmetric institutional workflows into open source infrastructure. Great article to read. -> A multi-agent LLM framework that simulates a hedge fund team analyzing stocks and generating trading signals, with support for Anthropic, OpenAI, and local models. -> A Python library for parsing and analyzing all major SEC EDGAR filing types (13F, ADV, 10-K, 8-K, 13D) with a built-in Claude MCP server for natural language queries.

2026 might be the year where hedge fund level research becomes accessible to literally anyone. Some random guy with Claude, a few regulatory PDFs and $12 of API credits can now do the kind of reverse engineering that used to require an actual team of analysts. Meanwhile most people still think AI is for anime pictures. This Jane Street / SEBI breakdown is one of the first articles in a while that actually feels like a glimpse of where things are going More like, AI turning highly asymmetric institutional workflows into open source infrastructure. Great article to read. -> A multi-agent LLM framework that simulates a hedge fund team analyzing stocks and generating trading signals, with support for Anthropic, OpenAI, and local models. -> A Python library for parsing and analyzing all major SEC EDGAR filing types (13F, ADV, 10-K, 8-K, 13D) with a built-in Claude MCP server for natural language queries.

34,875 Aufrufe • vor 3 Monaten

POLYMARKET BOTS ARE NOT BLACK BOXES -> Official Python client for Polymarket CLOB V2. If your pipeline is built in Python, this is probably the cleanest starting point: API creds, authenticated client, order flow, and direct CLOB interaction. -> A TypeScript library for Polymarket WebSocket data. Auto reconnect, dynamic subscriptions, book events, price changes, tick size changes, and last trade price. This is exactly the kind of repo you need for Step 1: recording the order book. -> Official Polymarket agent skills repo. This fits the “Now you can use AI” part perfectly: auth, order placement, market reads, live data streams, position management, and WebSocket patterns. Basically, the bridge between Polymarket data and AI agents. Not bad starter kit.

POLYMARKET BOTS ARE NOT BLACK BOXES -> Official Python client for Polymarket CLOB V2. If your pipeline is built in Python, this is probably the cleanest starting point: API creds, authenticated client, order flow, and direct CLOB interaction. -> A TypeScript library for Polymarket WebSocket data. Auto reconnect, dynamic subscriptions, book events, price changes, tick size changes, and last trade price. This is exactly the kind of repo you need for Step 1: recording the order book. -> Official Polymarket agent skills repo. This fits the “Now you can use AI” part perfectly: auth, order placement, market reads, live data streams, position management, and WebSocket patterns. Basically, the bridge between Polymarket data and AI agents. Not bad starter kit.

11,588 Aufrufe • vor 3 Monaten

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