Daniel Stephano Teran

10 free GitHub repos for trading on Polymarket. Everything you need to automate and make your analysis and trading easier: 1. The largest dataset with over 107GB of real historical Polymarket trading data, analyzed by 5 students from Shanghai University. GitHub: github.com/SII-WANGZJ/Pol… 2. This bot automatically manages all your limit orders on Polymarket to maximize liquidity rewards. GitHub: github.com/lihanyu81/poly… 3. A Chinese weather bot that analyzes multiple sources, like forecasts, airport data and aviation observations in real time to generate a detailed weather report for a specific day and city. GitHub: github.com/yangyuan-zhen/… 4. This tool lets you pull historical data for any ever existed market with detailed statistics and charts. GitHub: github.com/warproxxx/poly… 5. An AI trading server that lets Claude analyze any market in real time, track price movements, suggest possible trades and even trade for you. GitHub: github.com/caiovicentino/… 6. A ready to use tool for building your own AI agents. GitHub: github.com/pydantic/pydan… 7. This repo collects and analyzes the full trading history of any Polymarket wallet, exports data to CSV with statistics and charts. GitHub: github.com/leolopez007/po… 8. An autonomous researcher that can investigate any topic and generate a detailed final report. GitHub: github.com/assafelovic/gp… 9. This tool analyzes everything that happened on the web over the last 30 days to help find useful patterns, connections and recent context. Very useful before trading. GitHub: github.com/mvanhorn/last3… 10. A huge collection of 100+ useful tools and services for Polymarket, from analytics tools and trading bots to AI agents and education resources. GitHub: github.com/aarora4/Awesom… All of these tools come with a detailed step by step setup and usage guide.

🚨 WHO IS THIS TRADER??? Most traders picked a winner and called it a day This guy mapped every single outcome of the German election And walked away with $41,000 clean Here's the full breakdown of how he structured it: > he didn't just bet on who wins > he built a position across every probable scenario simultaneously The base layer was obvious but sized correctly: > Union takes most seats at 90.9¢ ($274k → $302k) > AfD comes second but doesn't lead at 94.4¢ ($119k → $126k) Safe. High probability. Steady returns. But that's not where the real money was The edge was in the range bets everyone else ignored: > AfD at 20-25% of vote at 70.5¢ → +54.3% ROI > CDU/CSU at 25-30% of vote at 40.1¢ → +120.7% ROI The market was pricing these ranges as unlikely He disagreed. With size I broke down political range trading months ago and said exactly this The crowd prices binary outcomes The edge lives in the distribution This is what separates a real political trader from someone just following polls He's not asking "who wins?" He's asking "what does the full probability distribution look like and where is the market wrong?" Then he sizes each scenario by confidence High conviction? Big position Ambiguous range? Smaller hedge Every scenario covered Every mispricing exploited This exact structure is what Poly_Parlay was built for Instead of managing 4 separate positions across the same event, you combine the high-conviction legs into one parlay Here's what that math looks like: > Base bet alone = +10% PnL > Range bet alone = +120% PnL > Both combined into one parlay = +552% PnL minimum Same political analysis Same research But instead of collecting each return separately, you stack them into one position and multiply Bot access: t.me/poly_parlay_bo… This guy is currently accumulating 4 new markets (all of them are green PnL already) Probably the best opportunity to follow his moves and multiply your winnings One election. One parlay. Completely different outcome. His wallet: polymarket.com/profile/%40ker… Anyone can pick a winner But real traders map the whole field

Este desarrollador chino ejecutó Llama 70B localmente en un MacBook en un avión y, durante 11 horas completas sin internet, gestionó proyectos de clientes. Estaba sentado junto a la ventana en un vuelo transatlántico con un MacBook Pro M4 con 64 GB de memoria. El WiFi a bordo costaba $25 por el vuelo. Lo rechazó. Sin API en la nube, sin conexión a los servidores de Anthropic o OpenAI, sin internet en absoluto. Solo un Llama 3.3 70B local en bf16 y su propio script de orquestador. El modelo se ejecuta a través de llama.cpp. Velocidad de generación, 71 tokens por segundo. Contexto alrededor de 60.000 tokens. Uso de memoria, 48,6 GiB de 64. Batería al despegue, 3 horas 21 minutos. Y le dio al orquestador esta instrucción de sistema antes del despegue: "Eres un orquestador offline que se ejecuta en un solo MacBook. No hay red. Los únicos recursos que tienes son archivos locales en /Users/dev/work, el servidor de inferencia Llama 70B en localhost:8080 y un presupuesto de batería de 3 horas 21 minutos. Procesa la cola en /Users/dev/work/queue.jsonl (una tarea de cliente por línea). Para cada tarea: borrador → ejecutar evaluaciones locales → guardar artefacto en /Users/dev/work/done/. Guarda puntos de control de contexto cada 12 tareas para que puedas reanudar después de un cambio de batería. Detente solo con la cola vacía o cuando la batería baje del 5 %." Así que el sistema sabe exactamente en qué recursos se está ejecutando. Sabe que no tiene conexión con el mundo exterior durante las próximas 11 horas. Sabe que tiene memoria finita y una batería finita. Sabe que el humano no intervendrá hasta que el avión aterrice. El sistema se ejecuta en 1 bucle. Toma una tarea de la cola, la ejecuta a través de inferencia, guarda el artefacto, escribe un punto de control. Tarea tras tarea, así de simple. Y solo cuando la batería baja del 5 %, el orquestador hace una pausa automáticamente, espera a que el portátil cambie al banco de energía de respaldo y continúa desde el último punto de control. Aquí está lo que el sistema realmente escribe en su registro durante el vuelo: "guardado punto de control de contexto 8 de 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restaurado punto de control de contexto (pos_min = 488, pos_max = 50118)" "progreso de procesamiento de prompt: n_tokens = 50 / 60 818" "tarea 37016 completada | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Fuera de la ventana, nubes, cielo azul y sin WiFi. En la bandeja, 1 MacBook, una terminal abierta en 2 pantallas y un servidor de inferencia en localhost. Por lo que he observado, este es el flujo de trabajo de IA offline más limpio que he visto en el último año: 11 horas de vuelo, $0 por WiFi y toda la cola de clientes cerrada antes del aterrizaje.

Traders are realizing that they’re no longer just trading against hedge funds, but also against AI.

🚨THIS IS INFINITE MONEY PRINTER Chinese dev farming 5–15 min BTC markets Instead of big bets, he’s exploiting tiny pricing inefficiencies Buying underpriced outcomes, flipping them seconds later, and repeating it nonstop - 23,494 predictions - ~$800K total PnL - largest win ~$21.4K Full breakdown in my article below👇

Polymarket trading bot owners reading this guide: from $100 to $100,000

Después de esta barbaridad de semana y viendo las ganas que tenéis de que os expliquemos en directo y con más detalles la estrategia que hemos seguido esta semana para generar +$100 diarios, este miércoles a las 19:00h (España) vamos a explicarlo todo en directo! Reserva tu plaza aquí: addcal.io/e/hrk3a8c2591i Será en directo, 100% gratis y sin repetición. Un abrazo descentralizado 💛

HE BUILT A FULLY AUTOMATED TRADING BOT IN 34 MINUTES WITHOUT WRITING A SINGLE LINE OF CODE. Claude handled the logic, the regime switches, the safety checks, and the full pipeline from signal to execution.

this OpenClaw bot finds warehouses with old roofs, renders solar panels on their actual building, and books the owner a call, all on autopilot... here's how commercial roofers can close $2M+ deals before the solar tax break ends: - scans thousands of commercial roofs via satellite - scores each building by roof age & urgency - pulls exact panel count from Google Solar API - finds the real owner (not the property manager) - calculates their federal credit to the dollar - renders a video of panels materializing on their roof - ships a personalized proposal - fully automated end to end every day that passes is money off the table. reply "ROOF" + RT and i'll send you the full breakdown so you can build this too (must be following so i can DM)

🚨 Alguien acaba de resolver uno de los mayores problemas de Claude Code. Han creado una herramienta que elimina los límites de uso y evita que deje de funcionar cuando alcanzas el límite. Tiene más de 47k stars en GitHub, es gratis y open-source. Aquí te explico cómo instalarla 👇

Compartiendo 10 proyectos de código abierto gratuitos en GitHub que se pueden usar para transacciones en Polymarket Todos gratuitos, te pueden ayudar a automatizar las transacciones 1. Marco de backtesting para mercados de predicción: github.com/evan-kolberg/p… Basado en datos históricos reales de Polymarket y Kalshi, para backtestear estrategias de trading 2. Marco de trading multiagente: github.com/TauricResearch… Para configurar rápidamente un sistema completo de trading con IA 3. Herramienta de investigación de sentimiento de los últimos 30 días: github.com/mvanhorn/last3… Análisis automático de noticias, dinámicas sociales y datos de mercados de predicción de los últimos 30 días 4. Herramienta auxiliar de trading para Polymarket: github.com/FiatFiorino/po… Proporciona indicadores de tendencias de mercado para ayudar a juzgar la dirección del mercado 5. Herramienta de limpieza de datos web: github.com/firecrawl/fire… Convierte cualquier página web en datos estructurados limpios y utilizables 6. Marco de agente inteligente de IA de nivel de producción: github.com/pydantic/pydan… Para construir robots de trading que se puedan poner en funcionamiento 7. Plataforma de automatización de flujos de trabajo: github.com/n8n-io/n8n Para análisis de noticias, filtrado de información y configuración de procesos automatizados 8. Servidor de servicios MCP de Tavily: github.com/tavily-ai/tavi… Servicio de búsqueda con IA con capacidades de recuperación profesional integradas (enlace original completado) 9. Recolector y analizador de datos de billeteras: github.com/txbabaxyz/coll… Captura el historial completo de transacciones de cualquier billetera y lo analiza 10. Herramienta de recolección y predicción de datos de Binance: github.com/txbabaxyz/mlmo… Predice las tendencias del mercado y calcula la valoración razonable de los activos Todos seguros y completamente gratuitos, hermanos

You could literally: >read one article about Polymarket bots >understand why 92% of wallets are in the red >stop trading manually forever >build a bot that trades against other bots >launch it and forget one wallet did this in January with $1,000 three months later it has $1,135,744 wrote 3,000 words about what happens when 10,000 AI agents start trading against each other and why the only edge left is predicting what the bots will do next?

Claude pulled me out of a $2,300 hole Card declined. 18 days until payday. Empty fridge. Most people would open a job board. I opened a terminal. “68 million trades on Polymarket. Find every contract where the crowd is systematically wrong.” Turns out… the crowd is wrong every single day. Same mistakes. On repeat. Copytrade → t.me/KreoPolyBot?st… Claude surfaced four patterns: → Headlines vs base rates “Will the FDA approve drug X?” trading at 78¢ Claude pulled 12 years of data Real probability? ~34¢ → Quiet accumulation Mispricing detected Wallet tracker shows 96 hours of silent buying from large addresses Win rate jumps from 71% → 91% → Deep mispriced tails Contracts at 4¢ where the model screams 88% Enter quietly Exit at 84¢ $12 → $260 Again. And again. → Post-news exhaustion 6 hours after a hype spike Price overshoots by 9–14% Every time Then came the filters: → Edge < 8% → skip → Order book depth < $250 → skip → Sports markets → skip → < 40 historical comps → skip If it passed all four… I didn’t hesitate Week 1–2: +$1,140 Week 3–4: +$2,680 Week 5–8: +$5,920 237 trades 8 weeks +$9,740 Starting bankroll: $2,300 Tools: $20/month Debt cleared on day 12 Fridge restocked on day 13 The market repeats the same mistakes Claude just counts them And never forgets

How you can build a Polymarket BTC trading engine with Claude to $1000/day print > RSI (context) + MACD (momentum) + Stochastic (timing) + EMA (structure) + OBV (confirmation) + VWAP (value) + Volatility (filter) = a bot that stops guessing and executes a framework Build v1: signal-based engine • Use RSI, ATR and cross-exchange price divergence to catch whale moves • Subscribe to Polymarket's order book WebSocket for real-time data • Minimize API calls. Let the WebSocket do the heavy lifting • This approach works around 95% of the time • The remaining 5% will cost you serious money if you hold to resolution Fastest way to copy-trade him even with $10 using: @0xRicker" target="_blank" rel="nofollow noopener">kreo.app/@0xRicker The edge No strategy wins forever Patterns only appear during specific windows of the day Your job: find the window. Trade only then AI alone won't write a strategy for you You need to train it on your own order book data first

Free Claude script made me $30,901 GitHub → local setup → no API costs, no limits Bot runs: spread farming + copytrade + pump sniper + news edge 18 days → 73% win rate → $30K+ profit Full guide — dropping in my TG ↓ t.me/Discover_0x

He built a model that trades weather on Polymarket and brings him tens of thousands of dollars every month He’ll show exactly how it works, and you can use it too polymarket.com/?r=copy