The Silicon Trader: Building Your First Algorithm

1. The Strategy Logic (Alpha)

Before you write a line of code, you must have a hypothesis. Algorithms do not "find" money; they execute a logic that extracts money.

Mean Reversion: Betting that price will return to an average (e.g., "Buy when RSI < 30 and price is 2 standard deviations below the VWAP").
Momentum/Trend: Betting that a move will continue (e.g., "Buy when the 50 MA crosses above the 200 MA and volume is > 150% of average").
Arbitrage: Betting on the price difference between two identical assets (e.g., Bitcoin on Exchange A vs. Exchange B).

2. The Backtesting Lie (Curve Fitting)

This is where 99% of aspiring quants fail. You write a bot, run it on last year's data, and it shows 500% returns. You launch it, and it loses money immediately. Why? Overfitting.

You tweaked the parameters (like changing the RSI period from 14 to 12) until it fit past data perfectly. But past noise does not predict future signal.

The Protocol:

In-Sample: Train your bot on data from 2015-2020.
Out-of-Sample: Test it on 2021-2023. If the performance drops by more than 20%, your model is fake.

3. The Tech Stack

You do not need a supercomputer.

Level 1 (Beginner): TradingView (Pine Script). Good for visual backtesting and alerts. Cannot execute trades directly without a webhook bridge.
Level 2 (Intermediate): Python (Pandas/Backtrader) + API. Use libraries like 'ccxt' to connect to exchanges. This allows for complex data analysis.
Level 3 (Pro): C++ / FPGA. Used for microsecond execution. Overkill for retail strategies.

4. Execution & Slippage

Your backtest assumes you bought exactly at 1.0500. Real life is different.

Slippage: By the time your order reaches the server, the price moved to 1.0502.
Commissions: A strategy that makes $5 per trade but costs $6 in fees is a losing strategy.

Rule: Always factor in 2x the average spread + commission into your backtest logic. If the edge survives transaction costs, it is real.

Conclusion: Code is the Ultimate Discipline

You don't need to be a Google Engineer to trade systematically. Start simple. A robot that buys the S&P 500 when it crosses the 200-day Moving Average has historically outperformed 90% of hedge fund managers. Start there.