🎨 Palette: CLI UX Improvements

.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.
+
+## 2024-05-24 - Accessibility in CLI Tools
+**Learning:** While colors and emojis improve scannability, they can be inaccessible to screen readers or users with specific terminal configurations. Providing standard flags like `--quiet` (for reducing noise) and `--no-color` (for plain text) is a critical UX pattern for CLI tools, allowing users to tailor the experience to their needs.
+**Action:** Always include `--quiet` and `--no-color` flags in CLI applications to support diverse user needs and automation use cases.

.gitignore

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

bitcoin_trading_simulation.py

@@ -1,3 +1,4 @@
+import argparse
 import numpy as np
 import pandas as pd
 
@@ -9,6 +10,16 @@ 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 +34,7 @@ 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 +45,7 @@ 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.
@@ -49,7 +62,8 @@ 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.
     """
@@ -81,13 +95,28 @@ 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}, 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 (default: 60)')
+    parser.add_argument('--initial-cash', type=float, default=10000, help='Initial cash (default: 10000)')
+    parser.add_argument('--initial-price', type=float, default=50000, help='Initial Bitcoin price (default: 50000)')
+    parser.add_argument('--volatility', type=float, default=0.02, help='Volatility factor (default: 0.02)')
+    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)
@@ -96,11 +125,11 @@ 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
+    initial_cash = args.initial_cash
     profit = final_value - initial_cash
 
     # Compare with buy and hold strategy

requirements.txt

@@ -1,2 +1,4 @@
 numpy
 pandas
+pytest
+flake8

test_simulation.py

@@ -0,0 +1,33 @@
+import pandas as pd
+import pytest
+from bitcoin_trading_simulation import simulate_bitcoin_prices, calculate_moving_averages, generate_trading_signals
+
+def test_simulate_bitcoin_prices():
+    days = 100
+    prices = simulate_bitcoin_prices(days=days)
+    assert len(prices) == days
+    assert isinstance(prices, pd.Series)
+    assert prices.name == 'Price'
+
+def test_calculate_moving_averages():
+    prices = pd.Series([100] * 50, name='Price')
+    signals = calculate_moving_averages(prices, short_window=5, long_window=10)
+    assert 'short_mavg' in signals.columns
+    assert 'long_mavg' in signals.columns
+    assert 'price' in signals.columns
+
+def test_generate_trading_signals():
+    # Create a scenario where short crosses long
+    # signal: 0, 0, 1, 1
+    # positions: NaN, 0, 1, 0 (shifted by 1)
+
+    data = {
+        'price': [100, 100, 100, 100],
+        'short_mavg': [90, 95, 105, 110],
+        'long_mavg': [100, 100, 100, 100]
+    }
+    signals = pd.DataFrame(data)
+    signals = generate_trading_signals(signals)
+
+    assert 'positions' in signals.columns
+    assert 'signal' in signals.columns