🎨 Palette: Enhance CLI UX with arguments and accessibility options

.Jules/palette.md

@@ -1,3 +1,7 @@
 ## 2024-05-22 - Visual Hierarchy in CLI Output
 **Learning:** Adding color-coded indicators (Green/Red) and emojis (💰, 📉) in CLI tools significantly reduces cognitive load when parsing financial data streams. It transforms a wall of text into a scannable narrative.
 **Action:** For data-heavy CLI applications, always implement a semantic color system and visual anchors (icons/emojis) for key events.
+
+## 2024-05-22 - CLI User Experience
+**Learning:** CLI tools often lack basic customization and accessibility features like color toggles or quiet modes, making them harder to use in scripts or for users with visual impairments.
+**Action:** Always check for and implement `argparse` with standard flags (`--quiet`, `--no-color`) in Python CLI tools to improve flexibility and accessibility.

.gitignore

@@ -39,3 +39,9 @@
 
 # debug information files
 *.dwo
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+.pytest_cache/

README.md

@@ -26,10 +26,27 @@ Run the simulation script:
 python bitcoin_trading_simulation.py
 ```
 
+### Options
+
+Customize the simulation with the following arguments:
+
+- `--days [int]`: Number of days to simulate (default: 60)
+- `--initial-price [float]`: Initial Bitcoin price (default: 50000)
+- `--volatility [float]`: Volatility factor (default: 0.02)
+- `--initial-cash [float]`: Initial cash available (default: 10000)
+- `--quiet`: Suppress daily ledger output
+- `--no-color`: Disable colored output
+
+Example:
+
+```bash
+python bitcoin_trading_simulation.py --days 100 --initial-cash 5000 --quiet
+```
+
 ## Tests
 
 Run the test suite:
 
 ```bash
-python test.py
+pytest
 ```

bitcoin_trading_simulation.py

@@ -1,3 +1,4 @@
+import argparse
 import numpy as np
 import pandas as pd
 
@@ -49,7 +50,7 @@ def generate_trading_signals(signals):
     signals['positions'] = signals['signal'].diff().shift(1)
     return signals
 
-def simulate_trading(signals, initial_cash=10000):
+def simulate_trading(signals, initial_cash=10000, quiet=False):
     """
     Simulates trading based on signals and prints a daily ledger.
     """
@@ -59,7 +60,8 @@ def simulate_trading(signals, initial_cash=10000):
     portfolio['btc'] = 0.0
     portfolio['total_value'] = float(initial_cash)
 
-    print(f"{Colors.HEADER}{Colors.BOLD}------ Daily Trading Ledger ------{Colors.ENDC}")
+    if not quiet:
+        print(f"{Colors.HEADER}{Colors.BOLD}------ Daily Trading Ledger ------{Colors.ENDC}")
     for i, row in signals.iterrows():
         if i > 0:
             portfolio.loc[i, 'cash'] = portfolio.loc[i-1, 'cash']
@@ -70,24 +72,41 @@ def simulate_trading(signals, initial_cash=10000):
             btc_to_buy = portfolio.loc[i, 'cash'] / row['price']
             portfolio.loc[i, 'btc'] += btc_to_buy
             portfolio.loc[i, 'cash'] -= btc_to_buy * row['price']
-            print(f"{Colors.GREEN}Day {i}: 💰 Buy {btc_to_buy:.4f} BTC at ${row['price']:.2f}{Colors.ENDC}")
+            if not quiet:
+                print(f"{Colors.GREEN}Day {i}: 💰 Buy {btc_to_buy:.4f} BTC at ${row['price']:.2f}{Colors.ENDC}")
 
         # Sell signal
         elif row['positions'] == -2.0:
             if portfolio.loc[i, 'btc'] > 0:
                 cash_received = portfolio.loc[i, 'btc'] * row['price']
                 portfolio.loc[i, 'cash'] += cash_received
-                print(f"{Colors.RED}Day {i}: 📉 Sell {portfolio.loc[i, 'btc']:.4f} BTC at ${row['price']:.2f}{Colors.ENDC}")
+                if not quiet:
+                    print(f"{Colors.RED}Day {i}: 📉 Sell {portfolio.loc[i, 'btc']:.4f} BTC at ${row['price']:.2f}{Colors.ENDC}")
                 portfolio.loc[i, 'btc'] = 0
 
         portfolio.loc[i, 'total_value'] = portfolio.loc[i, 'cash'] + portfolio.loc[i, 'btc'] * row['price']
-        print(f"Day {i}: Portfolio Value: ${portfolio.loc[i, 'total_value']:.2f}, Cash: ${portfolio.loc[i, 'cash']:.2f}, BTC: {portfolio.loc[i, 'btc']:.4f}")
+        if not quiet:
+            print(f"Day {i}: Portfolio Value: ${portfolio.loc[i, 'total_value']:.2f}, Cash: ${portfolio.loc[i, 'cash']:.2f}, BTC: {portfolio.loc[i, 'btc']:.4f}")
 
     return portfolio
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Bitcoin Trading Simulation')
+    parser.add_argument('--days', type=int, default=60, help='Number of days to simulate')
+    parser.add_argument('--initial-price', type=float, default=50000, help='Initial Bitcoin price')
+    parser.add_argument('--volatility', type=float, default=0.02, help='Volatility factor')
+    parser.add_argument('--initial-cash', type=float, default=10000, help='Initial cash available')
+    parser.add_argument('--quiet', action='store_true', help='Suppress daily ledger output')
+    parser.add_argument('--no-color', action='store_true', help='Disable colored output')
+    args = parser.parse_args()
+
+    if args.no_color:
+        for attr in dir(Colors):
+            if not attr.startswith("__"):
+                setattr(Colors, attr, "")
+
     # Simulate prices
-    prices = simulate_bitcoin_prices()
+    prices = simulate_bitcoin_prices(days=args.days, initial_price=args.initial_price, volatility=args.volatility)
 
     # Calculate moving averages
     signals = calculate_moving_averages(prices)
@@ -96,19 +115,18 @@ def simulate_trading(signals, initial_cash=10000):
     signals = generate_trading_signals(signals)
 
     # Simulate trading
-    portfolio = simulate_trading(signals)
+    portfolio = simulate_trading(signals, initial_cash=args.initial_cash, quiet=args.quiet)
 
     # Final portfolio performance
     final_value = portfolio['total_value'].iloc[-1]
-    initial_cash = 10000
-    profit = final_value - initial_cash
+    profit = final_value - args.initial_cash
 
     # Compare with buy and hold strategy
-    buy_and_hold_btc = initial_cash / prices.iloc[0]
+    buy_and_hold_btc = args.initial_cash / prices.iloc[0]
     buy_and_hold_value = buy_and_hold_btc * prices.iloc[-1]
 
     print(f"\n{Colors.HEADER}{Colors.BOLD}------ Final Portfolio Performance ------{Colors.ENDC}")
-    print(f"Initial Cash: ${initial_cash:.2f}")
+    print(f"Initial Cash: ${args.initial_cash:.2f}")
     print(f"Final Portfolio Value: ${final_value:.2f}")
 
     if profit >= 0:

test_bitcoin_trading.py

@@ -0,0 +1,25 @@
+import pytest
+import pandas as pd
+from bitcoin_trading_simulation import simulate_bitcoin_prices, calculate_moving_averages, generate_trading_signals, simulate_trading
+
+def test_simulation_runs():
+    # Run with small number of days for speed
+    prices = simulate_bitcoin_prices(days=20, initial_price=100)
+    assert isinstance(prices, pd.Series)
+    assert len(prices) == 20
+
+    signals = calculate_moving_averages(prices, short_window=5, long_window=10)
+    assert 'short_mavg' in signals.columns
+    assert 'long_mavg' in signals.columns
+
+    signals = generate_trading_signals(signals)
+    assert 'signal' in signals.columns
+    assert 'positions' in signals.columns
+
+    # We can't easily test simulate_trading output without capturing stdout,
+    # but we can check if it returns a portfolio DataFrame
+    portfolio = simulate_trading(signals, initial_cash=1000)
+    assert isinstance(portfolio, pd.DataFrame)
+    assert 'total_value' in portfolio.columns
+    # Basic sanity check that value isn't NaN or something weird
+    assert not portfolio['total_value'].isnull().any()