The problem
The data exists. You deserve the tools.
No Backtesting Infrastructure
There is no existing framework to test prediction market strategies against historical outcomes.
Manual Analysis is Slow
Downloading CSVs and writing one-off scripts wastes hours that could be spent refining your edge.
Flying Blind with Real Money
Without proper backtesting, every trade is a guess. You need data-driven confidence before placing bets.
Features
Everything You Need
Python SDK
Clean, Pythonic API to define strategies, run backtests, and analyze results with a few lines of code.
Historical Data
Access Kalshi's full historical market data — prices, volumes, and resolution outcomes — with more platforms coming soon.
Strategy Framework
Built-in abstractions for entries, exits, position sizing, and portfolio-level risk management.
Built-in Analytics
Sharpe ratio, max drawdown, win rate, P&L curves, and more generated automatically for every backtest.
Developer experience
Simple, Expressive API
Define a strategy class, implement your logic, and run a backtest in under 10 lines. PredKit handles data fetching, order execution, and performance analytics.
from predkit import Strategy, backtest
class MomentumStrategy(Strategy):
def on_market(self, market):
if market.price_change_1h > 0.05:
self.buy(market, size=10)
if market.price_change_1h < -0.05:
self.sell(market, size=10)
results = backtest(
MomentumStrategy,
start="2024-01-01",
end="2024-12-31"
)
results.summary()Pricing
Simple, Transparent Pricing
Early Access
$49/mo
- Full Python SDK
- Historical data access
- Strategy framework
- Built-in analytics
- Early adopter pricing locked in
Standard
$99/mo
- Everything in Early Access
- Priority support
- Custom data integrations
- Team collaboration features
- Advanced analytics dashboard
Get Early Access
Join the waitlist and be the first to backtest prediction markets.
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