Assumption:
假设: 人来下单。对接行情，MACD线，BOLL线

Certainly! Let’s create a simple Python program to implement a basic trading strategy. We’ll use a simple moving average crossover strategy, which is a common and straightforward approach in quantitative trading. In this strategy, we’ll use two moving averages: a short-term moving average (e.g., 5 periods) and a long-term moving average (e.g., 20 periods). When the short-term moving average crosses above the long-term moving average, it’s a signal to buy. When it crosses below, it’s a signal to sell.

Here’s a simple implementation:

1. Install necessary libraries:
   bash pip install pandas numpy

2. Create the trading strategy:

import pandas as pd
import numpy as np

# Sample data for demonstration (you should replace this with actual market data)
data = {
    'timestamp': pd.date_range(start='2023-05-01', periods=100, freq='H'),
    'close': np.random.randn(100).cumsum() + 3700  # Random walk around 3700
}
df = pd.DataFrame(data)

# Parameters
short_window = 5
long_window = 20
leverage = 10
trading_fee = 0.0008

# Calculate moving averages
df['short_mavg'] = df['close'].rolling(window=short_window).mean()
df['long_mavg'] = df['close'].rolling(window=long_window).mean()

# Generate signals
df['signal'] = 0
df['signal'][short_window:] = np.where(
    df['short_mavg'][short_window:] > df['long_mavg'][short_window:], 1, 0)
df['position'] = df['signal'].diff()

# Function to execute trades
def execute_trades(df):
    for i in range(len(df)):
        if df['position'][i] == 1:
            print(f"Buy at {df['close'][i]:.2f}")
        elif df['position'][i] == -1:
            print(f"Sell at {df['close'][i]:.2f}")

# Apply the trading strategy
execute_trades(df)

# Calculate performance (simple approach)
initial_balance = 10000
balance = initial_balance
position = 0

for i in range(len(df)):
    if df['position'][i] == 1:  # Buy
        position = balance * leverage / df['close'][i]
        balance -= balance * leverage * trading_fee
    elif df['position'][i] == -1:  # Sell
        balance += position * df['close'][i]
        balance -= position * df['close'][i] * trading_fee
        position = 0

final_balance = balance + position * df['close'].iloc[-1]
print(f"Initial Balance: ${initial_balance:.2f}")
print(f"Final Balance: ${final_balance:.2f}")
print(f"Net Profit: ${final_balance - initial_balance:.2f}")

Explanation:

1. Data Preparation:
   • We use a sample dataset with random walk data around 3700. Replace this with actual market data.
2. Parameters:
   • short_window and long_window define the periods for the moving averages.
   • leverage is set to 10x.
   • trading_fee is 0.08% (0.0008 in decimal form).
3. Moving Averages:
   • We calculate the short-term and long-term moving averages.
4. Signals:
   • We generate buy (1) and sell (-1) signals based on the crossover of the moving averages.
5. Execute Trades:
   • We print out buy and sell actions based on the signals.
6. Performance Calculation:
   • We simulate the balance changes based on the trades, taking into account the leverage and trading fees.

This is a basic implementation. In a real-world scenario, you would need to handle more complexities such as slippage, more advanced risk management, and integration with a trading API.