🎨 Palette: Add CLI 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.
+
+## 2024-10-24 - CLI Accessibility and Configuration
+**Learning:** Hardcoded values and mandatory color codes limit CLI usability and accessibility. `--no-color` supports monochrome terminals/parsing, and `argparse` empowers users.
+**Action:** Always wrap CLI entry points with argument parsing and provide flags to disable purely visual features like color.

.gitignore

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

bitcoin_trading_simulation.py

@@ -1,6 +1,8 @@
+import argparse
 import numpy as np
 import pandas as pd
 
+
 class Colors:
     HEADER = '\033[95m'
     BLUE = '\033[94m'
@@ -9,6 +11,7 @@ class Colors:
     ENDC = '\033[0m'
     BOLD = '\033[1m'
 
+
 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 +26,7 @@ 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 +37,7 @@ 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 +49,13 @@ 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.
     """
@@ -59,7 +65,9 @@ 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']
@@ -70,43 +78,71 @@ 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:
+            msg = (f"Day {i}: Portfolio Value: ${portfolio.loc[i, 'total_value']:.2f}, "
+                   f"Cash: ${portfolio.loc[i, 'cash']:.2f}, BTC: {portfolio.loc[i, 'btc']:.4f}")
+            print(msg)
+
     return portfolio
 
+
+def parse_arguments():
+    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='Volatility factor')
+    parser.add_argument('--quiet', '-q', action='store_true', help='Suppress daily ledger output')
+    parser.add_argument('--no-color', action='store_true', help='Disable colored output')
+    return parser.parse_args()
+
+
 if __name__ == "__main__":
+    args = parse_arguments()
+
+    if args.no_color:
+        Colors.HEADER = ''
+        Colors.BLUE = ''
+        Colors.GREEN = ''
+        Colors.RED = ''
+        Colors.ENDC = ''
+        Colors.BOLD = ''
+
     # 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}")