Part 4: Building Trading Logic Using Python
After understanding market structure, support and resistance, and building a simple trading strategy, the next step is to convert these ideas into structured trading logic using Python.
At this stage, the goal is not to build a full trading system yet, but to learn how trading rules can be expressed in a way that a computer can understand.
Why Python?
Python is a simple programming language widely used in data analysis and trading research. It helps us move from manual decision-making into structured logic.
From Strategy to Logic
A trading strategy in natural language:
Buy in uptrend, buy at support, sell in downtrend.
But a computer cannot understand this. We must convert it into logic rules.
Simple Trading Logic Example (Python)
# Simple trading logic example def check_trend(highs, lows): if highs[-1] > highs[-2] and lows[-1] > lows[-2]: return "UPTREND" elif highs[-1] < highs[-2] and lows[-1] < lows[-2]: return "DOWNTREND" else: return "RANGE" def trading_signal(price, support, resistance, trend): if trend == "UPTREND" and price <= support: return "BUY" if trend == "DOWNTREND" and price >= resistance: return "SELL" return "NO TRADE" # Example data highs = [1.1000, 1.1050, 1.1100] lows = [1.0900, 1.0950, 1.1000] price = 1.1010 support = 1.1000 resistance = 1.1100 trend = check_trend(highs, lows) signal = trading_signal(price, support, resistance, trend) print("Trend:", trend) print("Signal:", signal)
Why This Step Matters
This step forces us to define clear rules. If a strategy cannot be written in logic, it cannot be automated later.
Conclusion
Python acts as a bridge between trading ideas and automation.
In the next post, we will work with real market data using CSV and OHLC files.
