BR Academy

Python for Finance and Crypto Data: pandas, APIs and Backtesting

Learn Python for crypto and finance: pull live API data, build trading signals, backtest strategies, deploy to production.

Beginner38 lessons12.9 hoursCertificate

About this course

You have a trading idea. You’ve tested it in a spreadsheet. But you don’t know if it works on real data, and you can’t run it 24/7.

This course teaches you Python for finance and crypto — the way traders and analysts actually work. You’ll pull live price and on-chain data from Binance, CoinGecko, Dune, and Etherscan. You’ll clean messy time series, calculate technical indicators and portfolio metrics, and backtest your strategy on years of historical data *without look-ahead bias*. Then you’ll deploy it to a server and run it live.

No theory. No toy examples. Every lesson is a worked example with real data and real code you can reuse.

**What you’ll build:**
– A data pipeline that pulls live OHLCV candles and on-chain data from public APIs
– Technical indicators (moving averages, RSI, MACD, Bollinger Bands) from scratch
– A backtesting engine that handles slippage, commissions, and position sizing
– A trading strategy you can test, optimize, and deploy
– A live monitoring dashboard and trade log
– A scheduled job that runs your strategy every day

**Who this is for:** crypto traders automating their analysis, finance analysts moving from Excel to Python, students learning quantitative finance, and anyone who wants to test trading ideas on real data.

**Prerequisites:** basic Python (variables, loops, functions, imports). No prior experience with APIs, pandas, or backtesting required.

**What you’ll learn:** pull live and historical data from Binance, CoinGecko, Dune, and Etherscan; clean, resample, and merge time series data with pandas; calculate technical indicators and portfolio metrics; build a proper backtest with realistic costs; optimize parameters without overfitting; deploy to a server and monitor live trades; handle errors, log results, and iterate.

8 modules, 38 lessons, capstone project — build and deploy a complete live trading strategy from data pull to monitored deployment.

What you will be able to do

Can pull live and historical price data from Binance, CoinGecko, Dune Analytics and Etherscan APIs using Python requests and authentication
Can load, clean, resample and merge time series data using pandas (handling missing values, duplicates, timezone issues)
Can calculate technical indicators (moving averages, RSI, MACD, Bollinger Bands) and portfolio metrics (returns, drawdown, Sharpe ratio, win rate) from scratch
Can design and backtest a trading strategy on historical data without look-ahead bias, using proper train/test splits
Can visualize market data and backtest results with matplotlib and seaborn
Can schedule Python jobs to run daily (cron, APScheduler) and log results
Can read and interpret on-chain data (transactions, token holders, gas prices) from blockchain explorers and Dune dashboards

Curriculum 8 modules · 38 lessons

1Python Essentials for Finance5 lessons

Set up Python, pip, Jupyter and VS Code for data work Preview12 min
NumPy arrays, broadcasting, and vectorized math for speed 18 min
Series and DataFrames: the core data structures for time series 20 min
Load CSV, JSON, Parquet; export results 15 min
Print debugging, assertions, and unit tests for data pipelines 14 min

2Pulling Data from APIs5 lessons

Making GET/POST requests; API keys and headers 16 min
Pull spot prices, OHLCV candles, and order book from Binance 22 min
CoinGecko, Messari, and other free crypto data sources 18 min
Query transactions, token holders, gas prices from Etherscan 20 min
Run SQL queries on Dune and fetch results via API 19 min

3Cleaning, Resampling and Merging Time Series5 lessons

Detect and fill NaN, forward-fill, interpolation 17 min
Remove duplicates, detect and handle outliers 16 min
Parse dates, convert timezones, align timestamps 18 min
Resample OHLCV to different frequencies (1m to 1h to 1d) 19 min
Join price, volume, and on-chain data by timestamp 20 min

4Indicators, Signals and Portfolio Analytics5 lessons

SMA, EMA, and trend detection 18 min
RSI, MACD, Stochastic oscillator 20 min
Bollinger Bands, ATR, and volatility measures 19 min
Calculate cumulative returns, max drawdown, Sharpe ratio 21 min
Win rate, profit factor, consecutive wins/losses 17 min

5Backtesting Strategies Properly5 lessons

Build a backtest loop; avoid look-ahead bias 25 min
Implement entry signals, stop-loss, take-profit 22 min
Kelly criterion, fixed fractional, risk per trade 20 min
Model realistic costs and fills 18 min
Grid search, walk-forward, out-of-sample testing 23 min

6Visualization and Reporting5 lessons

Line plots, candlestick charts, subplots 19 min
Heatmaps, distributions, correlation matrices 18 min
Plotly for interactive dashboards 17 min
Equity curve, drawdown chart, trade log, metrics summary 20 min
Save plots, generate HTML reports, version control 16 min

7Scheduling and Production5 lessons

Schedule Python scripts with cron and APScheduler 19 min
Log trades, errors, and performance metrics 18 min
Graceful failures, retry logic, alerting 17 min
Run on a VPS or cloud (AWS, Heroku, DigitalOcean) 20 min
Paper trading, order execution, risk controls 21 min

8Capstone Project u2014 Build and Deploy a Live Strategy3 lessons

Design a complete trading strategy from data to live execution 30 min
Write the full pipeline: data pull, signal, backtest, live 60 min
Deploy to a server, monitor performance, iterate 30 min

Taught by Block Relations

Research & Education · Built for crypto traders, finance analysts, and quantitative finance students who know basic Python syntax (loops, functions, imports) but have never pulled real data from an API or built a backtest. No prior experience with APIs, pandas, or backtesting required.