Welcome to Keeks

Keeks is a Python library for bet sizing and bankroll simulation, with a portfolio-allocation layer for sizing many options together.

Size a single bet. Given your estimated win probability, payoff, loss, and transaction cost, Keeks’ strategies — the Kelly Criterion and eight variants — return the fraction of your bankroll to stake, and simulators replay that same rule over repeated trials to show the bankroll path it produces.

Size a portfolio. For portfolios of options described by return distributions rather than single bets, the allocation layer builds one joint-return model from what you have and sizes the portfolio at once, returning one long-only weight per option.

Disclaimer: This library is for educational purposes only. It is not intended to provide investment, legal, or tax advice. Always be responsible and consult with a professional before applying these strategies to real-world betting or investment scenarios. The authors and contributors of this library are not liable for any financial losses or damages that may result from the use of this software.

Installation

pip install keeks

Quick Example

from keeks.bankroll import BankRoll
from keeks.binary_strategies.kelly import KellyCriterion
from keeks.simulators.repeated_binary import RepeatedBinarySimulator

# Create a bankroll with initial funds
bankroll = BankRoll(initial_funds=1000.0, max_transaction_loss=0.3)

# Create a Kelly Criterion strategy
strategy = KellyCriterion(payoff=1.0, loss=1.0, transaction_cost_rate=0.01)

# Create a simulator with a fixed probability
simulator = RepeatedBinarySimulator(
    payoff=1.0,
    loss=1.0,
    fee_per_bet=0.01,
    probability=0.55,  # 55% chance of winning
    trials=1000
)

# Run the simulation
simulator.evaluate_strategy(strategy, bankroll)

# Plot the results
bankroll.plot_history()

Available Strategies

Keeks implements nine betting strategies:

  • Kelly Criterion: The mathematically optimal strategy for maximizing the logarithm of wealth

  • Fractional Kelly: A more conservative version of Kelly that reduces volatility

  • Drawdown-Adjusted Kelly: A Kelly variant that adjusts bet sizing based on risk tolerance

  • OptimalF (Ralph Vince): Strategy that maximizes geometric growth rate

  • Fixed Fraction: Simple strategy that bets a constant percentage of the bankroll

  • CPPI (Constant Proportion Portfolio Insurance): Strategy that protects a floor value while allowing upside exposure

  • Dynamic Bankroll Management: Adaptive strategy based on recent performance

  • MertonShare: A CRRA risk-aversion rule adapted to binary outcomes

  • Naive Strategy: A simple strategy that bets the full amount when expected value is positive

For portfolios of distribution-valued options, the allocation layer provides mean-variance, risk budgeting, HRP, mean-CVaR, and online methods over one configurable joint-return input model.

Development

The source code for Keeks is hosted on GitHub:

git clone https://github.com/wdm0006/keeks.git
cd keeks
pip install -e ".[dev]"

References

Getting Started

Learn

Core Reference

Portfolio Allocation

Contributing