import yfinance as yf
import pandas as pd
data = (
['2013/1/24', 2320.26, 2320.26, 2287.3, 2362.94],
['2013/1/25', 2300, 2291.3, 2288.26, 2308.38],
['2013/1/28', 2295.35, 2346.5, 2295.35, 2346.92],
['2013/1/29', 2347.22, 2358.98, 2337.35, 2363.8],
['2013/1/30', 2360.75, 2382.48, 2347.89, 2383.76],
['2013/1/31', 2383.43, 2385.42, 2371.23, 2391.82],
['2013/2/1', 2377.41, 2419.02, 2369.57, 2421.15],
['2013/2/4', 2425.92, 2428.15, 2417.58, 2440.38],
['2013/2/5', 2411, 2433.13, 2403.3, 2437.42],
['2013/2/6', 2432.68, 2434.48, 2427.7, 2441.73],
['2013/2/7', 2430.69, 2418.53, 2394.22, 2433.89],
['2013/2/8', 2416.62, 2432.4, 2414.4, 2443.03],
['2013/2/18', 2441.91, 2421.56, 2415.43, 2444.8],
['2013/2/19', 2420.26, 2382.91, 2373.53, 2427.07],
['2013/2/20', 2383.49, 2397.18, 2370.61, 2397.94],
['2013/2/21', 2378.82, 2325.95, 2309.17, 2378.82],
['2013/2/22', 2322.94, 2314.16, 2308.76, 2330.88],
['2013/2/25', 2320.62, 2325.82, 2315.01, 2338.78],
['2013/2/26', 2313.74, 2293.34, 2289.89, 2340.71],
['2013/2/27', 2297.77, 2313.22, 2292.03, 2324.63],
['2013/2/28', 2322.32, 2365.59, 2308.92, 2366.16],
['2013/3/1', 2364.54, 2359.51, 2330.86, 2369.65],
['2013/3/4', 2332.08, 2273.4, 2259.25, 2333.54],
['2013/3/5', 2274.81, 2326.31, 2270.1, 2328.14],
['2013/3/6', 2333.61, 2347.18, 2321.6, 2351.44],
['2013/3/7', 2340.44, 2324.29, 2304.27, 2352.02],
['2013/3/8', 2326.42, 2318.61, 2314.59, 2333.67],
['2013/3/11', 2314.68, 2310.59, 2296.58, 2320.96],
['2013/3/12', 2309.16, 2286.6, 2264.83, 2333.29],
['2013/3/13', 2282.17, 2263.97, 2253.25, 2286.33],
['2013/3/14', 2255.77, 2270.28, 2253.31, 2276.22],
['2013/3/15', 2269.31, 2278.4, 2250, 2312.08],
['2013/3/18', 2267.29, 2240.02, 2239.21, 2276.05],
['2013/3/19', 2244.26, 2257.43, 2232.02, 2261.31],
['2013/3/20', 2257.74, 2317.37, 2257.42, 2317.86],
['2013/3/21', 2318.21, 2324.24, 2311.6, 2330.81],
['2013/3/22', 2321.4, 2328.28, 2314.97, 2332],
['2013/3/25', 2334.74, 2326.72, 2319.91, 2344.89],
['2013/3/26', 2318.58, 2297.67, 2281.12, 2319.99],
['2013/3/27', 2299.38, 2301.26, 2289, 2323.48],
['2013/3/28', 2273.55, 2236.3, 2232.91, 2273.55],
['2013/3/29', 2238.49, 2236.62, 2228.81, 2246.87],
['2013/4/1', 2229.46, 2234.4, 2227.31, 2243.95],
['2013/4/2', 2234.9, 2227.74, 2220.44, 2253.42],
['2013/4/3', 2232.69, 2225.29, 2217.25, 2241.34],
['2013/4/8', 2196.24, 2211.59, 2180.67, 2212.59],
['2013/4/9', 2215.47, 2225.77, 2215.47, 2234.73],
['2013/4/10', 2224.93, 2226.13, 2212.56, 2233.04],
['2013/4/11', 2236.98, 2219.55, 2217.26, 2242.48],
['2013/4/12', 2218.09, 2206.78, 2204.44, 2226.26],
['2013/4/15', 2199.91, 2181.94, 2177.39, 2204.99],
['2013/4/16', 2169.63, 2194.85, 2165.78, 2196.43],
['2013/4/17', 2195.03, 2193.8, 2178.47, 2197.51],
['2013/4/18', 2181.82, 2197.6, 2175.44, 2206.03],
['2013/4/19', 2201.12, 2244.64, 2200.58, 2250.11],
['2013/4/22', 2236.4, 2242.17, 2232.26, 2245.12],
['2013/4/23', 2242.62, 2184.54, 2182.81, 2242.62],
['2013/4/24', 2187.35, 2218.32, 2184.11, 2226.12],
['2013/4/25', 2213.19, 2199.31, 2191.85, 2224.63],
['2013/4/26', 2203.89, 2177.91, 2173.86, 2210.58],
['2013/5/2', 2170.78, 2174.12, 2161.14, 2179.65],
['2013/5/3', 2179.05, 2205.5, 2179.05, 2222.81],
['2013/5/6', 2212.5, 2231.17, 2212.5, 2236.07],
['2013/5/7', 2227.86, 2235.57, 2219.44, 2240.26],
['2013/5/8', 2242.39, 2246.3, 2235.42, 2255.21],
['2013/5/9', 2246.96, 2232.97, 2221.38, 2247.86],
['2013/5/10', 2228.82, 2246.83, 2225.81, 2247.67],
['2013/5/13', 2247.68, 2241.92, 2231.36, 2250.85],
['2013/5/14', 2238.9, 2217.01, 2205.87, 2239.93],
['2013/5/15', 2217.09, 2224.8, 2213.58, 2225.19],
['2013/5/16', 2221.34, 2251.81, 2210.77, 2252.87],
['2013/5/17', 2249.81, 2282.87, 2248.41, 2288.09],
['2013/5/20', 2286.33, 2299.99, 2281.9, 2309.39],
['2013/5/21', 2297.11, 2305.11, 2290.12, 2305.3],
['2013/5/22', 2303.75, 2302.4, 2292.43, 2314.18],
['2013/5/23', 2293.81, 2275.67, 2274.1, 2304.95],
['2013/5/24', 2281.45, 2288.53, 2270.25, 2292.59],
['2013/5/27', 2286.66, 2293.08, 2283.94, 2301.7],
['2013/5/28', 2293.4, 2321.32, 2281.47, 2322.1],
['2013/5/29', 2323.54, 2324.02, 2321.17, 2334.33],
['2013/5/30', 2316.25, 2317.75, 2310.49, 2325.72],
['2013/5/31', 2320.74, 2300.59, 2299.37, 2325.53],
['2013/6/3', 2300.21, 2299.25, 2294.11, 2313.43],
['2013/6/4', 2297.1, 2272.42, 2264.76, 2297.1],
['2013/6/5', 2270.71, 2270.93, 2260.87, 2276.86],
['2013/6/6', 2264.43, 2242.11, 2240.07, 2266.69],
['2013/6/7', 2242.26, 2210.9, 2205.07, 2250.63],
['2013/6/13', 2190.1, 2148.35, 2126.22, 2190.1]
);
# 检查数据
print(data.head())
# 设置日期为索引
data.reset_index(inplace=True)
data.set_index('Date', inplace=True)
# 处理缺失值（前向填充）
data.fillna(method='ffill', inplace=True)
# 计算日收益率
data['Daily_Return'] = data['Close'].pct_change() * 100
# 计算不同周期的移动平均线
windows = [5, 20, 60] # 短期、中期、长期
for window in windows:
data[f'SMA_{window}'] = data['Close'].rolling(window=window).mean()
# 计算EMA（更灵敏）
data['EMA_20'] = data['Close'].ewm(span=20, adjust=False).mean()
# 计算标准差
data['STD_20'] = data['Close'].rolling(window=20).std()
# 计算上下轨
data['Upper_Band'] = data['SMA_20'] + (data['STD_20'] * 2)
data['Lower_Band'] = data['SMA_20'] - (data['STD_20'] * 2)
import matplotlib.pyplot as plt
plt.figure(figsize=(16,8))
plt.plot(data['Close'], label='Close Price', color='blue')
plt.plot(data['SMA_5'], label='5-Day SMA', color='orange')
plt.plot(data['SMA_20'], label='20-Day SMA', color='green')
plt.plot(data['SMA_60'], label='60-Day SMA', color='red')
plt.title('Stock Price with Moving Averages')
plt.xlabel('Date')
plt.ylabel('Price (CNY)')
plt.legend()
plt.grid(True)
plt.show()
# 计算价格与MA的偏离百分比
data['MA_Deviation'] = (data['Close'] - data['SMA_20']) / data['SMA_20'] * 100
# 绘制偏离度分布
plt.figure(figsize=(16,6))
plt.plot(data['MA_Deviation'], label='Deviation from 20-Day SMA', color='purple')
plt.axhline(0, color='black', linestyle='--')
plt.title('Price Deviation from 20-Day SMA (%)')
plt.xlabel('Date')
plt.ylabel('Deviation (%)')
plt.legend()
plt.grid(True)
import plotly.express as px
fig = px.line(data, x=data.index, y=['Close', 'SMA_20', 'SMA_60'],
title='Interactive Price & MA Chart',
labels={'value': 'Price (CNY)', 'variable': 'Indicator'})
fig.add_annotation(text="金叉：短期MA上穿长期MA", x='2023-06-01', y=1800, showarrow=True)
fig.show()
import numpy as np
# 计算对数收益率
data['Log_Return'] = np.log(data['Close'] / data['Close'].shift(1))
# 年化波动率（假设252个交易日）
volatility = data['Log_Return'].std() * np.sqrt(252)
print(f'年化波动率: {volatility:.2f}%')
# 计算真实波幅（True Range）
data['High_Low'] = data['High'] - data['Low']
data['High_Close'] = abs(data['High'] - data['Close'].shift(1))
data['Low_Close'] = abs(data['Low'] - data['Close'].shift(1))
data['TR'] = data[['High_Low', 'High_Close', 'Low_Close']].max(axis=1)
# 计算14日ATR
data['ATR'] = data['TR'].rolling(14).mean()
# 生成买入/卖出信号
data['Signal'] = 0
data['Signal'][5:] = np.where(data['SMA_5'][5:] > data['SMA_20'][5:], 1, -1)
# 计算策略收益
data['Strategy_Return'] = data['Close'].pct_change() * data['Signal'].shift(1)
plt.figure(figsize=(16,8))
plt.plot(data['Close'], label='Actual Price', color='blue')
plt.plot(data['SMA_5'], label='5-Day SMA', color='orange')
plt.plot(data['SMA_20'], label='20-Day SMA', color='green')
# 标注交易信号
buy_signals = data[data['Signal'] == 1].index
sell_signals = data[data['Signal'] == -1].index
plt.scatter(buy_signals, data['Close'][buy_signals], marker='^', color='green', label='Buy')
plt.scatter(sell_signals, data['Close'][sell_signals], marker='v', color='red', label='Sell')
plt.title('MA Crossover Strategy Signals')
plt.legend()
plt.grid(True)
最新发布
本文主要介绍了C++中的explicit关键字，详细解释了如何使用该关键字来防止构造函数被用于隐式类型转换，但仍然允许显式的类型转换。
扫一扫
967
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
