Tutorial
How to Backtest a Trading Thesis in 5 Minutes
You have a market idea. Maybe you think AI infrastructure will outperform, or that rate cuts will boost REITs. But how do you know if it would actually work? You backtest it.
What is backtesting?
Backtesting is running your trading strategy against historical market data to see how it would have performed. It answers the question: “If I had traded this idea over the past year, would I have made money?”
It doesn’t predict the future — but it does tell you whether your thesis has historical edge.
The old way (3+ days)
# The traditional approach
1. Learn Python (weeks)
2. Find a data source (yfinance, Alpaca, Polygon)
3. Clean and format the data
4. Write entry/exit logic
5. Handle position sizing, commissions, slippage
6. Build a performance report
7. Debug for 2 days when results look wrong
# Total: 3-7 days minimum
The AlgoThesis way (3 minutes)
1
Type your thesis
"AI chip demand will triple NVDA revenue by 2027"
10 seconds
2
AI discovers tickers + catalysts
Finds NVDA, AMD, TSM, AVGO + upcoming earnings dates
30 seconds
3
AI builds 3 strategy variations
Momentum, mean reversion, and catalyst-driven approaches
60 seconds
4
Backtests on 1 year of real data
P&L, Sharpe ratio, max drawdown, alpha vs S&P 500
7 seconds
5
Review results, deploy or iterate
Paper trade, go live on Alpaca, or refine your thesis
Your call
What to look for in results
P&L %
> 0%
Did it make money?
Sharpe Ratio
> 1.0
Return per unit of risk
Max Drawdown
< 20%
Worst peak-to-trough drop
Alpha
> 0%
Beat buy-and-hold S&P?
Common backtesting mistakes
- Survivorship bias: Only testing stocks that exist today. Delistings and bankruptcies are excluded, inflating returns.
- Overfitting: Adding so many rules that your strategy fits past data perfectly but fails on new data.
- Ignoring costs: Not accounting for commissions, slippage, and spread. AlgoThesis includes these automatically.
- Look-ahead bias: Using information that wasn't available at the time of the trade.