我让matplotlib显示图表，但蜡烛显示为黑色垂直线，而不是绿色/红色的开放/高/低/关闭
数据从Binance API中检索，图表图片显示正常，除了垂直黑线。
寻找帮助，看到正常的蜡烛与开放/高/低/关闭信息。
x1c 0d1x的数据

import requests
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

# Binance API endpoint for Kline data
url = 'https://api.binance.com/api/v3/klines'

# Parameters for the API request
symbol = 'BTCUSDT'
interval = '1d'
limit = 200  # Number of Klines to retrieve (max: 1000)

# Prepare the request parameters
params = {
    'symbol': symbol,
    'interval': interval,
    'limit': limit
}

# Send the GET request to the Binance API
response = requests.get(url, params=params)
data = response.json()

# Extracting the relevant data from the API response
timestamps = [entry[0] / 1000 for entry in data]
opens = [float(entry[1]) for entry in data]
highs = [float(entry[2]) for entry in data]
lows = [float(entry[3]) for entry in data]
closes = [float(entry[4]) for entry in data]

# Convert timestamps to readable dates
dates = [mdates.epoch2num(timestamp) for timestamp in timestamps]

# Plotting the candlestick chart
fig, ax = plt.subplots()
ax.xaxis_date()
candlestick_data = list(zip(dates, opens, highs, lows, closes))
ax.vlines(dates, lows, highs, color='black', linewidth=2)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))

# Calculate the moving averages
moving_average_7 = np.convolve(closes, np.ones(7) / 7, mode='valid')
moving_average_20 = np.convolve(closes, np.ones(20) / 20, mode='valid')
moving_average_50 = np.convolve(closes, np.ones(50) / 50, mode='valid')

# Determine the corresponding dates for the moving averages
moving_average_dates_7 = dates[7 - 1:]  # Subtract 1 to align the dates with moving average values
moving_average_dates_20 = dates[20 - 1:]  # Subtract 1 to align the dates with moving average values
moving_average_dates_50 = dates[50 - 1:]  # Subtract 1 to align the dates with moving average values

# Plotting the moving averages
ax.plot(moving_average_dates_7, moving_average_7, color='red', linewidth=1, label='7-day Moving Average')
ax.plot(moving_average_dates_20, moving_average_20, color='blue', linewidth=1, label='20-day Moving Average')
ax.plot(moving_average_dates_50, moving_average_50, color='orange', linewidth=1, label='50-day Moving Average')

# Calculate Bollinger Bands
period = 20  # Bollinger Bands period
std_dev = np.std(closes[-period:])  # Standard deviation for the period
middle_band = np.convolve(closes, np.ones(period) / period, mode='valid')  # Middle band is the moving average
upper_band = middle_band + 2 * std_dev  # Upper band is 2 standard deviations above the middle band
lower_band = middle_band - 2 * std_dev  # Lower band is 2 standard deviations below the middle band

# Plotting the Bollinger Bands
ax.plot(dates[period - 1:], upper_band, color='purple', linestyle='--', linewidth=1, label='Upper Band')
ax.plot(dates[period - 1:], lower_band, color='purple', linestyle='--', linewidth=1, label='Lower Band')

# Add the last values of the indicators to the right axis
ax.text(1.02, 0.7, f'Last Price: {closes[-1]:.2f}', transform=ax.transAxes, color='black')
ax.text(1.02, 0.6, f'MA7: {moving_average_7[-1]:.2f}', transform=ax.transAxes, color='red')
ax.text(1.02, 0.5, f'MA20: {moving_average_20[-1]:.2f}', transform=ax.transAxes, color='blue')
ax.text(1.02, 0.4, f'MA50: {moving_average_50[-1]:.2f}', transform=ax.transAxes, color='orange')

plt.xticks(rotation=45)
plt.title(f'Kline Chart for {symbol} - {interval}')
plt.xlabel('Date')
plt.ylabel('Price (USDT)')
plt.legend()
plt.grid(True)
plt.show()

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