Custom Python-based automated trading systems for Polymarket, Kalshi, Betfair and other leading prediction and betting platforms — probability modelling, market making, arbitrage and Kelly-optimal staking strategies.
Deep API expertise across the most liquid and reputable prediction markets and betting exchanges available today.
The world's largest decentralised prediction market — built on Polygon. Binary and multi-outcome markets on political events, economics, sports, crypto, and world events. CLOB (Central Limit Order Book) with on-chain settlement, high liquidity, and a fully accessible REST and WebSocket API for algorithmic trading.
The first CFTC-regulated prediction market in the United States — offering legally compliant event contracts on politics, economics, weather, and financial events. Institutional-grade API with order book access, making it the go-to platform for US-based algo traders in the prediction market space.
The world's largest sports betting exchange with deep liquidity across football, horse racing, tennis, cricket, and hundreds of other markets. Betfair's Exchange API allows full algorithmic access — real-time price streaming, automated order placement, and market data — making it a serious platform for systematic sports traders.
A prominent US political prediction market focused primarily on election outcomes, legislative events, and political appointments. Offers REST API access for automated trading with a strong community of politically-informed traders — creating mispricing opportunities for quantitative models.
Every strategy is custom-built with rigorous probability modelling, backtested on historical market data, and deployed with full bankroll and risk controls.
Builds independent probability models using historical data, news feeds, and statistical signals to estimate the true probability of an event outcome. When the market price diverges significantly from the model's estimate, the system automatically places trades to exploit the mispricing before the market corrects — the core alpha source in prediction market trading.
Automated liquidity provision on Polymarket and Kalshi CLOB order books — simultaneously posting YES and NO bids within the spread to earn the bid-ask spread over a high volume of trades. Includes dynamic spread adjustment based on market volatility, inventory management to control directional exposure, and toxicity filters to avoid adverse selection from informed traders.
Real-time monitoring of the same event across multiple platforms — Polymarket, Kalshi, and PredictIt — to identify pricing discrepancies for the same outcome. When the same event trades at materially different prices across platforms, the system simultaneously buys on the cheaper platform and sells on the more expensive one, locking in a near risk-free profit.
Natural Language Processing models that monitor real-time news, social media, and official data releases to detect events that should materially shift the probability of a market outcome before the crowd reacts. For example — detecting an election poll release, economic data print, or breaking political news and immediately adjusting positions on Polymarket or Kalshi before prices move.
Systematic sports trading on Betfair Exchange using statistical models for pre-match and in-play markets. Covers football (expected goals models), horse racing (form and speed ratings), and tennis (serve statistics). Strategies include pre-match value betting, in-play scalping, and laying overpriced favourites using ELO-based and regression models.
Mathematically optimal position sizing using the Kelly Criterion — calculating the exact fraction of bankroll to stake on each bet or trade based on estimated edge and probability. Includes fractional Kelly variants for risk reduction, dynamic bankroll tracking, and drawdown-adjusted staking that reduces exposure during losing runs across Betfair, Polymarket, and Kalshi.
Exploits overreactions in prediction market prices following breaking news or large directional trades. When a market price moves sharply but the underlying probability has not fundamentally changed — for example, a temporary shock from a misleading headline — the system fades the move and profits from the reversion to fair value.
Machine learning models (XGBoost, LightGBM, ensemble methods) trained on historical prediction market outcomes, polling data, economic indicators, and sentiment data to produce probability forecasts. Walk-forward validation ensures out-of-sample accuracy. Forecasts feed directly into automated trading execution on Polymarket, Kalshi, and PredictIt.
From API integration to probability modelling and live deployment — complete end-to-end development capability.
Full Polymarket REST and WebSocket integration — order placement, order book streaming, market data, position management, and on-chain settlement via Polygon. Handles wallet signing, gas optimisation, and USDC balance management.
Complete Kalshi REST API implementation — market discovery, order book data, order placement and management, account tracking, and settlement monitoring. Handles CFTC-compliant order flow and Kalshi's market lifecycle correctly.
Full Betfair Exchange API integration — real-time price streaming via the Streaming API, order placement, in-play market handling, and market catalogue management. Handles Betfair's authentication, session tokens, and rate limits correctly.
Custom statistical models built in Python using historical outcome data, polling APIs, economic data feeds, and sports statistics sources — producing calibrated probability estimates to compare against live market prices.
Live data ingestion from news APIs (NewsAPI, GDELT), social media (Twitter/X), official government data sources, and sports statistics feeds — parsed and processed in real time to update model inputs as events unfold.
Historical prediction market data collection and storage — including resolved market outcomes, price histories, and volume data — to build and validate strategy performance before live deployment.
Prediction and betting markets have unique risk characteristics — every system is built with controls designed specifically for these markets.
Mathematically optimal position sizing — staking the correct fraction of bankroll based on estimated edge. Fractional Kelly variants used to reduce variance while maintaining long-run growth.
Configurable daily and total bankroll drawdown limits that automatically pause all activity when losses reach defined thresholds — preventing an extended losing run from causing catastrophic damage.
Maximum exposure caps per individual event or market — preventing over-concentration in a single outcome regardless of how strong the model signal appears. Portfolio-level diversification enforced programmatically.
Trades only execute when the model's estimated edge exceeds a minimum threshold — filtering out marginal signals where the probability estimate may be unreliable. Confidence intervals tracked and updated continuously.
Telegram and email alerts for every trade execution, model update, connectivity issue, and daily P&L summary. Full visibility into exactly what the system is doing at all times.
Automated monitoring of model accuracy over time — detecting when prediction accuracy degrades below acceptable levels and pausing trading until the model is reviewed and recalibrated.
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