Example project · Python · Algorithmic · Research
A vectorised back-test of a 20/50 EMA crossover rule across several years of data for a sub-portfolio of Nifty 50 stocks, with a full performance report.
A vectorised back-test of a 20/50 EMA crossover rule across several years of data for a sub-portfolio of Nifty 50 stocks, with a full performance report.
A documented back-test with equity curve, drawdown chart and trade list.
State the 20/50 EMA crossover entry and exit rules unambiguously.
Compute the averages and the position series without look-ahead bias.
Apply the positions to returns and deduct assumed costs and slippage.
Produce the equity curve, drawdown chart and trade list, and compare with buy-and-hold.
Document every assumption and what the test cannot show.
AlphaSync is a simulated practice environment for education. Simulated and paper-trading environments do not reproduce all the characteristics of live markets, such as execution, liquidity, costs and emotional pressure. Simulated results do not represent actual trading and do not predict future results. Screens show illustrative data.
Algorithmic Trading
Fetch historical NSE data with Python, build a 20/50 EMA crossover rule, detect entries and exits, and back-test it honestly before moving to paper trading.
1 Apr 2026 · 5 min read
Projects are completed inside Academy courses, with instructor feedback. Enquire about the course that includes this project.