🎨 Palette: Add CLI arguments for customization and accessibility

.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.
+
+## 2024-05-23 - Accessibility and Customization in CLI Tools
+**Learning:** While rich CLI output is engaging, it can be problematic for accessibility or automated parsing. Providing flags like `--no-color` and `--quiet` ensures the tool is versatile and inclusive.
+**Action:** Always include `--no-color` and `--quiet` flags in CLI tools that use rich formatting.

.gitignore

@@ -39,3 +39,7 @@
 
 # debug information files
 *.dwo
+
+# Python
+__pycache__/
+*.pyc

README.md

@@ -26,6 +26,21 @@ Run the simulation script:
 python bitcoin_trading_simulation.py
 ```
 
+### Options
+
+- `--days`: Number of days to simulate (default: 60)
+- `--initial-cash`: Initial cash amount (default: 10000)
+- `--initial-price`: Initial Bitcoin price (default: 50000)
+- `--volatility`: Price volatility (default: 0.02)
+- `--quiet`: Suppress daily ledger output
+- `--no-color`: Disable colored output
+
+Example:
+
+```bash
+python bitcoin_trading_simulation.py --days 30 --initial-cash 5000 --quiet
+```
+
 ## Tests
 
 Run the test suite:

bitcoin_trading_simulation.py

@@ -1,5 +1,7 @@
 import numpy as np
 import pandas as pd
+import argparse
+
 
 class Colors:
     HEADER = '\033[95m'
@@ -9,6 +11,16 @@ class Colors:
     ENDC = '\033[0m'
     BOLD = '\033[1m'
 
+    @classmethod
+    def disable(cls):
+        cls.HEADER = ''
+        cls.BLUE = ''
+        cls.GREEN = ''
+        cls.RED = ''
+        cls.ENDC = ''
+        cls.BOLD = ''
+
+
 def simulate_bitcoin_prices(days=60, initial_price=50000, volatility=0.02):
     """
     Simulates Bitcoin prices for a given number of days using Geometric Brownian Motion.
@@ -23,6 +35,7 @@ def simulate_bitcoin_prices(days=60, initial_price=50000, volatility=0.02):
         prices.append(prices[-1] + price_change)
     return pd.Series(prices, name='Price')
 
+
 def calculate_moving_averages(prices, short_window=7, long_window=30):
     """
     Calculates short and long moving averages for a given price series.
@@ -33,6 +46,7 @@ def calculate_moving_averages(prices, short_window=7, long_window=30):
     signals['long_mavg'] = prices.rolling(window=long_window, min_periods=1, center=False).mean()
     return signals
 
+
 def generate_trading_signals(signals):
     """
     Generates trading signals based on the Golden Cross strategy.
@@ -44,12 +58,13 @@ def generate_trading_signals(signals):
     signals.loc[signals['short_mavg'] > signals['long_mavg'], 'signal'] = 1.0
     # A Death Cross (sell signal)
     signals.loc[signals['short_mavg'] < signals['long_mavg'], 'signal'] = -1.0
-    
+
     # We create 'positions' to represent the trading action: 1 for buy, -1 for sell, 0 for hold
     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.
     """
@@ -59,11 +74,13 @@ 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']
-            portfolio.loc[i, 'btc'] = portfolio.loc[i-1, 'btc']
+            portfolio.loc[i, 'cash'] = portfolio.loc[i - 1, 'cash']
+            portfolio.loc[i, 'btc'] = portfolio.loc[i - 1, 'btc']
 
         # Buy signal
         if row['positions'] == 2.0:
@@ -81,32 +98,49 @@ def simulate_trading(signals, initial_cash=10000):
                 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}, "
+                  f"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-cash", type=float, default=10000, help="Initial cash amount")
+    parser.add_argument("--initial-price", type=float, default=50000, help="Initial Bitcoin price")
+    parser.add_argument("--volatility", type=float, default=0.02, help="Price volatility")
+    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:
+        Colors.disable()
+
     # 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)
-    
+
     # Generate trading signals
     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
+    initial_cash = args.initial_cash
     profit = final_value - initial_cash
-    
+
     # Compare with buy and hold strategy
     buy_and_hold_btc = 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"Final Portfolio Value: ${final_value:.2f}")

test_bitcoin_trading.py

@@ -0,0 +1,45 @@
+import unittest
+import pandas as pd
+from bitcoin_trading_simulation import (
+    simulate_bitcoin_prices,
+    calculate_moving_averages,
+    generate_trading_signals,
+    simulate_trading
+)
+
+
+class TestBitcoinSimulation(unittest.TestCase):
+    def test_simulate_bitcoin_prices(self):
+        days = 10
+        prices = simulate_bitcoin_prices(days=days)
+        self.assertIsInstance(prices, pd.Series)
+        self.assertEqual(len(prices), days)
+
+    def test_calculate_moving_averages(self):
+        prices = simulate_bitcoin_prices(days=20)
+        signals = calculate_moving_averages(prices, short_window=5, long_window=10)
+        self.assertIsInstance(signals, pd.DataFrame)
+        self.assertIn('short_mavg', signals.columns)
+        self.assertIn('long_mavg', signals.columns)
+
+    def test_generate_trading_signals(self):
+        prices = simulate_bitcoin_prices(days=20)
+        signals = calculate_moving_averages(prices, short_window=5, long_window=10)
+        signals = generate_trading_signals(signals)
+        self.assertIn('signal', signals.columns)
+        self.assertIn('positions', signals.columns)
+
+    def test_simulate_trading(self):
+        prices = simulate_bitcoin_prices(days=20)
+        signals = calculate_moving_averages(prices, short_window=5, long_window=10)
+        signals = generate_trading_signals(signals)
+        portfolio = simulate_trading(signals, initial_cash=10000, quiet=True)
+        self.assertIsInstance(portfolio, pd.DataFrame)
+        self.assertIn('total_value', portfolio.columns)
+        self.assertIn('cash', portfolio.columns)
+        self.assertIn('btc', portfolio.columns)
+        self.assertEqual(len(portfolio), 20)
+
+
+if __name__ == '__main__':
+    unittest.main()