Trading backtests that account for hidden costs.
What it does
Guides the construction of trading-strategy backtests that account for biases and trading costs to estimate performance reliably.
When to use it
Use when developing trading backtests, building backtesting infrastructure, validating performance, addressing biases, implementing walk-forward analysis, or comparing strategies.
How to use it
It changes how the agent builds trading backtests by requiring point-in-time data, realistic transaction costs, and out-of-sample validation.
What you provide
- An existing project
What you need · 3
The worked implementation examples are Python code; no runtime version is specified.
The worked implementations import pandas for tabular price data and backtest results.
The worked implementations import numpy for numerical calculations and simulations.
Access · 0
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