import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load historical data (using sample data here; in practice, you can obtain from sources like Yahoo Finance)
# Assume we have a CSV file containing 'Date', 'Close Price', and 'Volume'
# df = pd.read_csv('stock_data.csv')

# For demonstration, we create a simulated dataset
date_range = pd.date_range(start='2024-01-01', end='2025-01-01', freq='B')
n = len(date_range)
closing_price = 100 + np.cumsum(np.random.randn(n))  # Randomly generate price data
volume = np.random.randint(100000, 1000000, n)  # Randomly generate volume data

# Artificially add some volume-price breakout signals
# Add large volume increases at several random positions
breakout_indices = np.random.choice(n, 5, replace=False)
for i in breakout_indices:
    closing_price[i] = closing_price[i-1] * (1 + np.random.uniform(0.02, 0.05))
    volume[i] = volume[i] * 3

# Create DataFrame
df = pd.DataFrame({
    'Date': date_range,
    'Close Price': closing_price,
    'Volume': volume
})

# Calculate 5-day and 20-day moving averages
df['MA5'] = df['Close Price'].rolling(window=5).mean()
df['MA20'] = df['Close Price'].rolling(window=20).mean()

# Calculate 5-day average volume and set volume anomaly threshold
df['Volume_MA5'] = df['Volume'].rolling(window=5).mean()
volume_threshold = 2  # Volume exceeding 2 times the 5-day average is considered abnormal

# Detect volume-price breakout signals
df['Volume-Price Breakout'] = False
for i in range(20, n):
    # Price breaks through 20-day MA and volume abnormally increases
    if (df['Close Price'].iloc[i] > df['MA20'].iloc[i] and 
        df['Volume'].iloc[i] > volume_threshold * df['Volume_MA5'].iloc[i]):
        df['Volume-Price Breakout'].iloc[i] = True

# Visualize results
plt.figure(figsize=(14, 7))
plt.subplot(2, 1, 1)
plt.plot(df['Date'], df['Close Price'], label='Close Price')
plt.plot(df['Date'], df['MA5'], label='5-day MA')
plt.plot(df['Date'], df['MA20'], label='20-day MA')
# Mark volume-price breakout signals
breakout_dates = df[df['Volume-Price Breakout']]['Date']
breakout_prices = df[df['Volume-Price Breakout']]['Close Price']
plt.scatter(breakout_dates, breakout_prices, color='red', marker='^', label='Volume-Price Breakout Signal')
plt.title('Price Trend and Volume-Price Breakout Signals')
plt.legend()

plt.subplot(2, 1, 2)
plt.bar(df['Date'], df['Volume'], label='Volume')
plt.plot(df['Date'], df['Volume_MA5'], color='red', label='5-day Average Volume')
plt.title('Volume and Average Volume')
plt.legend()

plt.tight_layout()
plt.show()

# Output dates when volume-price breakout signals occurred
print("Dates when volume-price breakout signals occurred:")
print(df[df['Volume-Price Breakout']][['Date', 'Close Price', 'Volume']])

Investment JournalBasic Information
Date: 2025-03-15
Market Environment: Consolidating downward
Investment Objective: Medium-term (3-6 months)
Target Analysis
Stock Code: 600519 (Kweichow Moutai)
Industry: Food and Beverage
Buying Reasons:

Reasonable valuation (P/E 28x, lower than historical average)
Stable fundamentals (continuous growth in revenue and profits)
Technical signs of stabilization (shrinking volume and stabilization, MACD golden cross)


Risk Factors:

Macroeconomic downward pressure
Intensifying industry competition


Trading Plan
Buying Price Range: 1650-1700 yuan
Buying Position: 10% of total funds
Stop-Loss Point: 1530 yuan (7% decline)
Take-Profit Target: 2000 yuan (18% increase) or use trailing take-profit
Actual Trading Record
Buying Date: 2025-03-16
Buying Price: 1680 yuan
Buying Quantity: 50 shares
Total Cost: 84,000 yuan
Follow-up Tracking
2025-03-20: Price 1720 yuan, up 2.38%, continue holding
2025-03-25: Price 1780 yuan, up 5.95%, approaching target, consider partial profit-taking
2025-04-01: Price 1850 yuan, up 10.12%, sell 20 shares, lock in partial profits
2025-04-10: Price 1980 yuan, up 17.86%, sell all, complete trade
Trading Summary
Final Return Rate: 17.86%
Successful Experience: Strictly executed trading plan, sold in batches when approaching target
Areas for Improvement: Could consider appropriately relaxing take-profit targets when fundamentals remain unchanged
Lessons Learned
When market volatility is high, staged buying and selling can effectively reduce risk
Fundamental analysis is the basis for long-term investment, while technical analysis can help grasp entry timing
Strict risk control is the key to investment success