A Staged Workflow for Developing and Deploying Trading Algorithms

Summary

This guide lays out a sequence for building an algorithmic strategy: define the trading idea and market, specify instrument filters, check the logic visually, backtest, tune parameters, forward test in simulation, and deploy. Its running example is a daily mean-reversion approach for liquid FMCG equities, using Bollinger Bands around a moving average, with entries and exits at specified band levels. It also suggests evaluating returns, drawdowns, trade counts, costs, slippage, and entry and exit rules.

The author illustrates the workflow with an ITC backtest and gives reported trade and return figures, as well as a visual review of historical prices. The article warns against using current prices to generate signals and cautions that parameter tuning can overfit historical conditions. Paper trading is presented as a way to observe behavior in a live environment without risking capital, followed by operational and technical deployment needs such as order, risk, and fund management and broker and data connections. The example is instructional, not evidence of robust out-of-sample performance.

Key ideas

• Define the strategy logic, market, trade frequency, data horizon, and tools before coding.
• Use instrument filters that match the strategy’s intended market conditions.
• Review signals visually and backtest with costs, slippage, drawdown, and trade-level measures.
• Avoid look-ahead errors by basing signals on available historical data.
• Use forward testing and account for operational controls before live deployment.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.