Before I risk real dollars on a Kalshi market, I want to know how a strategy would have performed historically. The problem is that prediction markets don't come with the same backtesting infrastructure as equity futures. So I built my own. It's not perfect, but it's better than trading blind. This post walks through how I approached building a Kalshi backtester in Python, the pitfalls I hit, and what I'd do differently.
Primary sources I checked: The Kalshi platform for market structure and contract specifications, the Kalshi API documentation for data endpoints and rate limits, and the CFTC designation order for understanding how Kalshi operates as a regulated DCM.
Some traders assume prediction markets are purely news-driven and therefore impossible to systematically test. I disagree. While event resolution is binary (yes or no), price behavior leading up to resolution often follows patterns worth studying.
A few things backtesting can help with:
A backtester won't tell you if the next Fed meeting will surprise hawkish. But it can tell you whether buying "Yes" contracts below 20 cents historically paid off when consensus shifted.
This is where most people get stuck. Kalshi's public API provides access to current market data and historical trades, but the depth of historical data varies. You'll need to pull trade history and order book snapshots if available.
Here's a basic Python structure to start pulling data:
import requests
BASE_URL = "https://trading-api.kalshi.com/trade-api/v2"
def get_market_history(ticker):
endpoint = f"{BASE_URL}/markets/{ticker}/history"
response = requests.get(endpoint)
return response.json()
A few practical notes:
If you're serious about building a Kalshi backtester in Python, budget time for data cleaning. Prediction market data is messier than futures tick data. Spreads can be wide, volume can be thin, and you'll see gaps.
I keep my backtester modular. Three main components:
Handles pulling, storing, and serving historical prices. I normalize everything to a common format: timestamp, ticker, bid, ask, last price, volume. For resolved markets, I add a settlement column (1 for Yes, 0 for No).
This is where your logic lives. Each strategy is a Python class with a simple interface:
class BaseStrategy:
def on_bar(self, timestamp, market_data):
# Return signal: 'buy', 'sell', or None
pass
You can build anything from simple threshold rules ("buy Yes if price drops below X") to more complex signals incorporating polling data or related markets.
This is critical and often underestimated. You need to model:
I default to assuming fills at the worse side of the spread. It's conservative, but prediction market liquidity can evaporate fast, especially near resolution.
If you've backtested equity strategies, prediction markets will feel different. A few things I learned the hard way:
Binary resolution changes everything. In futures, you can be directionally right but still lose money on timing. In prediction markets, you're either right or wrong at settlement. This makes win rate calculations more meaningful but also more brutal.
Markets have expiration dates. You can't hold a position past resolution. Your backtester needs to force-close positions at settlement and apply the correct payout (contract settles at $1 or $0).
Liquidity is inconsistent. Some Kalshi markets trade actively. Others sit with wide spreads for days. I filter out markets with fewer than a threshold number of trades per day when testing strategies that require frequent entries and exits.
News impact is asymmetric. A surprise jobs report might move a Fed rate market instantly. Your backtester can't simulate information you didn't have at the time. Be honest about what data was actually available at each timestamp.
The easiest way to build a profitable backtest is to overfit. I've done it. Everyone has. Here's how I try to stay honest:
I share observations and market notes in the Telegram channel I run, but I'm always clear that past performance in backtests doesn't guarantee anything forward. This is speculative trading.
After building a Kalshi backtester in Python over several iterations, a few lessons stand out:
First, I should have built better logging from day one. When a strategy underperforms, you need to know why, trade by trade. A simple CSV log with entry price, exit price, hold time, and resolution pays for itself.
Second, I underestimated how much time I'd spend on data issues. Mismatched timestamps, missing ticks, duplicate records. Budget 40% of your project time for data work.
Third, I wish I'd started with simpler strategies. My first attempt was too clever. A basic mean reversion rule on oversold markets would have taught me more about the infrastructure than a multi-factor model.
Kalshi's API offers access to market history and trade data, but the depth and granularity can vary by market. Some historical data is available through public endpoints. For comprehensive backtesting, you may need to collect and store data yourself over time. Check the official API documentation for current endpoints and limitations before building your data pipeline.
I rely on requests for API calls, pandas for data manipulation, and SQLite or PostgreSQL for storage. For visualization, matplotlib works fine. Some traders use backtrader or zipline as frameworks, but I found prediction market quirks (binary settlement, expiration) easier to handle with custom code. Start simple and add complexity only when needed.
Kalshi charges fees per contract, typically a few cents. Your execution simulator should deduct these fees on both entry and exit. Fee structures can change, so verify current rates on the Kalshi platform directly. Even small fees compound over many trades, so include them from the start or your backtest results will be unrealistically optimistic.
Currently, there's no plug-and-play backtesting tool designed specifically for Kalshi. Some traders adapt generic frameworks, but you'll still need to handle data ingestion and binary settlement logic. If you're not comfortable with Python, spreadsheets can work for very simple rule-based analysis, but they become unwieldy quickly. Learning basic Python is worth the investment if you plan to trade systematically.
Not financial advice. I trade my own money and you can lose yours. Do your own research.