Building a Simple +EV Scanner for Kalshi in Python

Building a Simple +EV Scanner for Kalshi in Python

In the rapidly evolving world of predictive markets, Kalshi offers an intriguing platform that allows users to trade on events. A fundamental aspect of successful trading is identifying situations where the odds offered by the market are greater than the actual probability of the event occurring, which is known as a positive expected value (+EV) opportunity. In this post, we will build a simple +EV scanner using Python, which will automate the identification of these trading opportunities.

Understanding +EV Opportunities

Before diving into the code, let’s clarify what we mean by +EV. In trading, +EV occurs when the potential payout of a trade outweighs the risk involved, calculated by the following formula:

[ EV = (P(win) \times Payout) - (P(loss) \times Loss) ]

Where:

• (P(win)) = Probability of winning the trade
• (Payout) = Amount won if the trade is successful
• (P(loss)) = Probability of losing the trade, which is (1 - P(win))
• (Loss) = Amount lost if the trade is unsuccessful

Setting Up Your Python Environment

To get started, you will need the following Python libraries: requests, pandas, and numpy. You can install them using pip:

pip install requests pandas numpy

Fetching Market Data from Kalshi

Kalshi provides an API that allows you to fetch current market data. To demonstrate the +EV scanner, we'll need to access the event market data and the current odds.

Here is a simple function to retrieve market data:

import requests

def fetch_market_data():
    url = "https://api.kalshi.com/v1/markets"  # Hypothetical endpoint (update to the actual one)
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error fetching data: {response.status_code}")
        return None

Analyzing Market Data for +EV Opportunities

Once we have the market data, we need to analyze it to identify any +EV opportunities. We will analyze each event to compute the expected value based on the odds.

First, let's set up a basic structure to store our events and their odds.

import pandas as pd

![Article illustration](https://sgpqsfrnctisvvexbaxi.supabase.co/storage/v1/object/public/blog-images/building-simple-ev-scanner-kalshi-python-body.png)

def analyze_market_data(data):
    events = []
    
    for market in data['markets']:
        for outcome in market['outcomes']:
            outcome_odds = outcome['probability']  # This should be a float between 0 and 1
            expected_value = calculate_ev(outcome_odds, market['payout'], market['loss'])
            if expected_value > 0:
                events.append({
                    "event": market['event'],
                    "outcome": outcome['name'],
                    "odds": outcome_odds,
                    "expected_value": expected_value
                })
    return pd.DataFrame(events)

def calculate_ev(win_probability, payout, loss):
    loss_probability = 1 - win_probability
    return (win_probability * payout) - (loss_probability * loss)

Example of +EV Calculation

Let’s say we have a market where the probability of an event happening is 0.6 (60% chance), and the payout is $100 while the loss is $50.

Using the function defined above, we can calculate the expected value:

win_probability = 0.6
payout = 100
loss = 50

ev = calculate_ev(win_probability, payout, loss)
print(f"Expected Value: {ev}")

This will provide a straightforward output that indicates whether the trade is profitable based on the inputs.

Resolving Multiple Events

For a practical scanner, your implementation should iterate through multiple events and aggregate all +EV opportunities. Here is an example of how to fetch and analyze market data:

def main():
    market_data = fetch_market_data()
    if market_data:
        results = analyze_market_data(market_data)
        print(results)
        
if __name__ == "__main__":
    main()

Automating Identifications with Alerts

To make the +EV scanner even more useful, you can implement a simple alert system. This can be done by adding an email notification or a message to a chat application when a new +EV opportunity is found.

The following example uses a hypothetical send_alert function to illustrate:

def send_alert(event):
    # Implement your alert mechanism (e.g., email, SMS)
    print(f"New +EV Opportunity: {event['event']} - {event['outcome']} with EV: {event['expected_value']}")

Then integrate it into your main function:

def analyze_market_data(data):
    events = []
    
    for market in data['markets']:
        for outcome in market['outcomes']:
            outcome_odds = outcome['probability']  
            expected_value = calculate_ev(outcome_odds, market['payout'], market['loss'])
            if expected_value > 0:
                event = {
                    "event": market['event'],
                    "outcome": outcome['name'],
                    "odds": outcome_odds,
                    "expected_value": expected_value
                }
                events.append(event)
                send_alert(event)  # Send an alert for each +EV opportunity
    return pd.DataFrame(events)

Conclusion

With this simple +EV scanner, we can continuously monitor Kalshi for profitable trading opportunities. With Python’s rich ecosystem of libraries and an API like Kalshi's, you can effectively automate your trading strategies and make data-driven decisions. Going further, you can enhance this scanner by including features like historical data analysis, real-time updates, and even implementing machine learning models to predict outcomes.

In the fast-paced world of trading, having a dedicated tool for +EV opportunities can give you an edge over the competition. Happy trading!