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Python Basics – Control Flow Utility

Overview

This utility teaches Python control flow structures essential for building trading algorithms and financial applications. Master conditionals, loops, and comprehensions to write efficient, readable code.

Concepts Covered

Conditional Statements

  • if/elif/else: Make decisions based on conditions
  • Nested conditionals: Complex decision trees
  • Comparison operators: >, <, >=, <=, ==, !=
  • Logical operators: and, or, not

For Loops

  • Iterating sequences: Lists, tuples, dictionaries
  • Range function: Generate number sequences
  • Enumerate: Get index and value
  • Dictionary iteration: Keys, values, items

While Loops

  • Condition-based loops: Run until condition is False
  • Infinite loops: Use with caution!
  • Loop control: break, continue

List Comprehensions

  • Concise syntax: Replace loops with one-liners
  • Filtering: Add conditionals to comprehensions
  • Dictionary comprehensions: Create dictionaries efficiently
  • Performance: Faster than traditional loops

Break and Continue

  • break: Exit loop early
  • continue: Skip current iteration
  • Use cases: Early termination, filtering

Key Examples

Risk Assessment with Conditionals

volatility = 0.25

if volatility < 0.15:
 risk_level = "Low"
elif volatility < 0.30:
 risk_level = "Medium"
else:
 risk_level = "High"

Portfolio Iteration

portfolio = {"AAPL": 50, "GOOGL": 20, "MSFT": 30}

for ticker, shares in portfolio.items():
 print(f"{ticker}: {shares} shares")

List Comprehensions for Returns

prices = [100, 102, 98, 101, 105]
pct_changes = [(prices[i] - prices[i-1]) / prices[i-1]
 for i in range(1, len(prices))]

Files

  • control_flow_tutorial.py: Interactive tutorial with finance examples

How to Run

python control_flow_tutorial.py

Practice Ideas

  1. Risk Management System
  2. Use conditionals to create a multi-level risk assessment

  3. Include position size limits based on account balance

  4. Backtesting Loop

  5. Iterate through historical prices

  6. Track entry/exit signals and P&L

  7. Portfolio Rebalancing

  8. Check if any allocation exceeds tolerance

  9. Calculate required trades to rebalance

  10. Watchlist Filter

  11. Use list comprehension to filter stocks by criteria

  12. (e.g., price > 50, volume > 1M, PE ratio < 20)

  13. Compound Interest Calculator

  14. Calculate balance year-by-year with a loop
  15. Find how many years to reach a target amount

Next Steps

  • Move to UTILS - Python Basics - Functions/ to learn function definitions
  • Explore UTILS - Data Structures - Lists/ for advanced list operations
  • Apply control flow in UTILS - Technical Indicators/ for real algorithms

Financial Applications

Trading Signals

# Multi-condition signal generation
if price > moving_average_50 and volume > avg_volume:
 if rsi < 70:
 signal = "BUY"
 else:
 signal = "OVERBOUGHT"
else:
 signal = "HOLD"

Stop Loss Monitoring

# Check prices until stop loss triggered
while current_price > stop_loss_price and not position_closed:
 current_price = get_latest_price()
 if current_price <= stop_loss_price:
 close_position()

Portfolio Analysis

# Find all holdings above target allocation
overweight = [ticker for ticker, allocation in allocations.items()
 if allocation > target_allocation[ticker]]

Master control flow to build dynamic trading systems and sophisticated financial applications!