大操手量化投资课堂基础篇及进阶篇全部源码合集

这是大操手量化投资课堂，基础入门篇以及进阶篇的全部源码合集，现在整理出来供大家免费下载

此篇内容的源码总览请看下图：

详细源码有：基础篇的pandas，numpy内容，比如用numbpy构造行情数据，并用python的画图工具画出五日均线。

以及进阶篇的talib的使用及tushare的使用，包括布林带线计算及可视化，以及macd计算及可视化等等。

布林带线：

import tushare as ts
import pandas as pd
import mplfinance as mpf
# 获取实盘数据，000001.SZ 平安银行
from talib import abstract

# 获取数据
pro = ts.pro_api()
data = pro.daily(ts_code='000001.SZ', start_date='20200101')
data.to_csv("000001.SZ.csv")
# 数据预处理
data_new = pd.read_csv("000001.SZ.csv", usecols=['trade_date', 'open', 'high','low', 'close',  'vol'])
# 指定索引
data_new.index=pd.to_datetime(data_new['trade_date'].astype("str"))
data_new.drop(columns=['trade_date'], inplace=True)
# 指定列
data_new.columns=['open', 'high','low', 'close',  'volume']
data_new.sort_index(inplace=True)

# 计算boll
BBANDS = abstract.Function('BBANDS')
# matype:0=SMA;1=EMA;3=DEMA;4=TEMA;5=TRIMA;6=KAMA;7=MAMA;8=T3;default=0=SMA
upperband, middleband, lowerband = BBANDS(data_new.close, timeperiod = 20, nbdevup=2.0, nbdevdn=2.0, matype=0)
print("上轨：\n",upperband)
print("中轨：\n",middleband)
print("下轨：\n",lowerband)

# 画图-可视化
add_plot = [
    # 上轨
    mpf.make_addplot(upperband, color='y'),
    # 中轨
    mpf.make_addplot(middleband, color='w'),
    # 下轨
    mpf.make_addplot(lowerband, color='m')
]
# 指定图形属性
my_color = mpf.make_marketcolors(up='red', down='cyan', volume='blue', inherit=True)
my_style = mpf.make_mpf_style(
    facecolor = 'black',
    marketcolors=my_color,
    gridaxis='horizontal',
    gridcolor='red',
    gridstyle='--',
    y_on_right=False
)
mpf.plot(data_new,
         addplot=add_plot,
         type='candle',
         style = my_style,
         figratio=(1920, 1080),
         figscale=1,
         volume=True,
         savefig='bbands_pic.png'
         )

MACD指标源码：

# coding=utf-8# !/usr/bin/python"""@Author: jeevan@微信公众号: 大操手量化投资@Site: http://dacaoshou.com@Software: pycharm@File：__init__.py.py@Time：2020/10/8 9:03@Description: """import tushare as tsimport pandas as pdimport mplfinance as mpf# 获取实盘数据，000001.SZ 平安银行from talib import abstractimport numpy as np# 获取数据pro = ts.pro_api()data = pro.daily(ts_code='000001.SZ', start_date='20200101')data.to_csv("000001.SZ.csv")# 数据预处理data_new = pd.read_csv("000001.SZ.csv", usecols=['trade_date', 'open', 'high','low', 'close',  'vol'])# 指定索引data_new.index=pd.to_datetime(data_new['trade_date'].astype("str"))data_new.drop(columns=['trade_date'], inplace=True)# 指定列data_new.columns=['open', 'high','low', 'close',  'volume']data_new.sort_index(inplace=True)#MACD计算MACD = abstract.Function('MACD')DIFF, DEA, MACDHIST = MACD(data_new.close, fastperiod=12, slowperiod=26, signalperiod=9)print(np.around(DIFF, decimals=2))print(np.around(DEA, decimals=2))print(np.around(MACDHIST*2, decimals=2))#macdhist值处理ORIGIN_MACD_UP = MACDHIST*2;ORIGIN_MACD_UP[ORIGIN_MACD_UP<0]=0MACD_POS= ORIGIN_MACD_UPORIGIN_MACD_DN= MACDHIST*2;ORIGIN_MACD_DN[ORIGIN_MACD_DN>0]=0MACD_NEG= ORIGIN_MACD_DN# 画图-可视化add_plot = [    # DIFF    mpf.make_addplot(DIFF, panel=2, color='fuchsia', secondary_y=True),    # DEA    mpf.make_addplot(DEA, panel=2, color='w', secondary_y=True),    # MACDHIST#mpf.make_addplot(MACDHIST, type='bar', panel=2, color='cyan', secondary_y=True)    mpf.make_addplot(MACD_POS, type='bar', panel=2, color='r', secondary_y=True),    mpf.make_addplot(MACD_NEG, type='bar', panel=2, color='cyan', secondary_y=True)]# 指定图形属性my_color = mpf.make_marketcolors(up='red', down='cyan', volume='blue', inherit=True)my_style = mpf.make_mpf_style(    facecolor = 'black',    marketcolors=my_color,    gridaxis='horizontal',    gridcolor='red',    gridstyle='--',    y_on_right=False)mpf.plot(data_new,         addplot=add_plot,         type='candle',         style = my_style,         figratio=(1920, 1080),         figscale=1.88,         volume=True,         main_panel=0,         volume_panel=1,         savefig='macd_pic.png'         )