0.1 — Project 1: Backtesting Engine (Foundations)#

Build a simple, reliable backtesting system from scratch. This project establishes the core skills you’ll need for all later quant projects: loading data, generating signals, and evaluating performance.

Objective#

Create a working backtest engine that can load historical data, simulate trades based on a strategy, and produce performance reports.

Focus Areas#

• Loading and cleaning historical market data
• Understanding time series
• Computing indicators
• Evaluating strategy performance

Example Strategies to Implement#

• Moving average crossover — Go long when short MA crosses above long MA; exit or short when it crosses below.
• Simple momentum strategy — Trade based on price or return momentum over a lookback window.

Technologies#

• Python — Core implementation
• Pandas — Data manipulation and time series
• yfinance (or another data source) — Historical market data
• Jupyter Notebooks — Exploration and reporting (optional)

Skills You’ll Learn#

• Data ingestion and cleaning
• Signal generation from indicators
• P&L calculation and trade simulation
• Performance metrics (returns, Sharpe, drawdown)
• Strategy evaluation and interpretation

Deliverable#

A working backtest engine that:

• Loads historical price data for one or more symbols
• Computes indicators (e.g., moving averages)
• Generates entry/exit signals
• Simulates trades (positions, P&L)
• Produces a performance report (returns, risk metrics, optional charts; optional: win rate, number of trades)

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Lessons in this chapter#

Lesson	Description
0.1 — Project overview (this page)	Objective, focus areas, steps, prerequisites, and hints
0.2 — Project guide	Step-by-step implementation guide with hints and collapsible solutions for each step

Work through the overview first, then use the Project guide as you build the engine.

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Prerequisites (with links to lessons)#

Before starting, you should be comfortable with:

Topic	Why you need it	Where to learn
Python basics	Core language for the project	Python & Pandas — start with Chapter 0 (0.1, 0.2)
Pandas	DataFrames, indexing, time series, loading data	Python & Pandas — 1.1 Series and DataFrame, 1.2 Indexing, 1.3 Time series and DatetimeIndex, 1.4 Loading and cleaning market data
Basic statistics	Returns, volatility, Sharpe ratio for performance metrics	Applied Statistics — 1.1 Returns and volatility for backtesting
Probability foundations	Interpreting strategy results, risk, and uncertainty	Quant Research — Introduction (see “Interpreting backtest results”)

All of these subjects are available from the homepage; study them in any order, then return here to implement the backtester.

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Steps to Complete the Project#

1. Set up the environment
   Create a project folder (e.g. projects/01_backtester), a virtual environment, and install pandas, numpy, and yfinance. Optionally add matplotlib or plotly for charts.

2. Load and clean historical data
   Use yfinance (or another source) to download daily OHLCV data for at least one symbol. Handle missing values, align dates, and ensure a single timezone. Store the result in a Pandas DataFrame with a DatetimeIndex.

3. Compute indicators
   Implement at least one indicator (e.g., simple moving average). Add columns to your DataFrame for the indicator values. Validate against a known reference (e.g., first few values by hand or another library).

4. Define and implement signal logic
   From your indicators, define clear rules for when to enter long, exit, and optionally short. Implement this as a function that returns a series of signals (e.g., +1, -1, 0) or explicit entry/exit timestamps.

5. Simulate trades
   Walk through the signal series and the price series to compute position sizes and P&L. Assume a simple rule (e.g., fixed notional per trade, or full equity). Track equity curve and trade list.

6. Compute performance metrics
   From the equity curve and/or trade list, compute total return, annualized return, volatility (e.g., annualized std of returns), and Sharpe ratio (or similar). Optionally add max drawdown and win rate.

7. Produce a report
   Output a summary (print or notebook): key metrics and, if you added plotting, an equity curve and/or drawdown chart.

8. Document and refactor
   Add a short README describing how to run the backtest and what the strategy does. Clean up the code so it’s readable and reusable for the next project.

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Hints (with backlinks)#

• Data shape — Keep one row per date and one column per series (open, high, low, close, volume, then your indicators). See 1.1 Series and DataFrame and 1.3 Time series and DatetimeIndex.
• Avoiding look-ahead bias — Compute indicators and signals only from past data at each timestamp. Use “past only” slices as in 1.2 Indexing and selection; see Quant Research — Introduction for interpreting results.
• P&L and slippage — Start with close-to-close returns and no slippage. Later you can add a simple slippage model (e.g., fixed bps per trade). See Project guide — Simulate trades for implementation.
• Performance metrics — Use the definitions in Returns and volatility for backtesting: total return, annualized return and volatility, and Sharpe ratio.
• Testing — Sanity-check on a known period: e.g., a simple buy-and-hold should show positive return in a bull market. Compare your MA crossover against a manual calculation for a few bars.

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Goals#

By the end of this project you should be able to:

• Load and clean historical OHLCV data in Pandas
• Compute at least one indicator and generate signals
• Simulate a strategy and produce an equity curve
• Report basic performance metrics and interpret them
• Use this engine as the foundation for Project 2 (paper trading bot)

Good luck—once this engine works, you’ll extend it in the next project with live data and real order execution (paper only).