VWAP-Anchored Tiered Exit

Starter is $49/month with 30 deploys per week. Upgrade as you grow.

Trades Kalshi 15-minute XRP markets, evaluated every 10 seconds, with a 20-contract cap and price bounds 0.01–0.99. Rules run top-to-bottom, first match fires. Entries buy 5 contracts of YES or NO only under their full conditions: above/below VWAP plus EMA, momentum and velocity filters at 0.1–0.65 price with spread at most 0.015; acceleration entries at 0.2–0.8; late-window entries with 5 minutes or less remaining at 0.4–0.6 (with VWAP, EMA/SMA confirmation) or 0.88/0.12 (with 15-minute high/low and price above 15). Exits sell all, tiered by position size and profit (over 3, 5, 10, 15) or loss (under 5, 10, 12, 15), with the two smallest tiers also requiring price between 0.10 and 0.90.

Kalshi·Sep 15 to Oct 15 UTC·Created 11d ago

Net P&L

+$470

Return +2348.3% on Risk Capital

Sharpe

0.23

Return Sharpe

Win rate

61.3%

8253 trades

Max drawdown

-$391

Peak to Trough

A few notes on these numbers

Methodology: Backtests replay supported historical market data, usually a 30-day Studio window, and evaluate strategy rules on the available cadence. Signals can fill no earlier than the next tradable step. Taker fills use top-of-book prices or available L2 depth, with Kalshi-style fees where modeled. Queue position, latency, and future liquidity are not fully modeled. Simulated P&L is net of modeled fees over the window.
Starting Capital: Kalshi backtests use the strategy's configured max position as the risk-capital denominator. Return is computed from that configured capital, while total P&L is still shown in dollars.
Past Performance: Backtested results are hypothetical. They are derived from historical data and do not reflect live execution risks like slippage beyond the orderbook, partial fills, API latency, or venue outages. Past performance does not guarantee future results.
Not Financial Advice: Nothing on this page is investment, legal, or tax advice. Prediction markets carry real risk of loss. Only trade with money you can afford to lose, and do your own research before deploying any strategy live.