用Python做股市数据分析

import pandas as pd

import pandas.io.data as web # Package and modules for importing data; this code may change depending on pandas version

import datetime

# We will look at stock prices over the past year, starting at January 1, 2016

start = datetime.datetime(2016,1,1)

end = datetime.date.today()

# Let's get Apple stock data; Apple's ticker symbol is AAPL

# First argument is the series we want, second is the source ("yahoo" for Yahoo! Finance), third is the start date, fourth is the end date

apple = web.DataReader("AAPL", "yahoo", start, end)

type(apple)

C:\Anaconda3\lib\site-packages\pandas\io\data.py:35: FutureWarning:

The pandas.io.data module is moved to a separate package (pandas-datareader) and will be removed from pandas in a future version.

After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import ``from pandas.io import data, wb`` to ``from pandas_datareader import data, wb``.

FutureWarning)

pandas.core.frame.DataFrame

import matplotlib.pyplot as plt # Import matplotlib

# This line is necessary for the plot to appear in a Jupyter notebook

%matplotlib inline

# Control the default size of figures in this Jupyter notebook

%pylab inline

pylab.rcParams['figure.figsize'] = (15, 9) # Change the size of plots

apple["Adj Close"].plot(grid = True) # Plot the adjusted closing price of AAPL

from matplotlib.dates import DateFormatter, WeekdayLocator,\

DayLocator, MONDAY

from matplotlib.finance import candlestick_ohlc

def pandas_candlestick_ohlc(dat, stick = "day", otherseries = None):

"""

:param dat: pandas DataFrame object with datetime64 index, and float columns "Open", "High", "Low", and "Close", likely created via DataReader from "yahoo"

:param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include "day", "week", "month", and "year", ("day" default), and any numeric input indicates the number of trading days included in a period

:param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines

This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.

"""

mondays = WeekdayLocator(MONDAY) # major ticks on the mondays

alldays = DayLocator() # minor ticks on the days

dayFormatter = DateFormatter('%d') # e.g., 12

# Create a new DataFrame which includes OHLC data for each period specified by stick input

transdat = dat.loc[:,["Open", "High", "Low", "Close"]]

if (type(stick) == str):

if stick == "day":

plotdat = transdat

stick = 1 # Used for plotting

elif stick in ["week", "month", "year"]:

if stick == "week":

transdat["week"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1]) # Identify weeks

elif stick == "month":

transdat["month"] = pd.to_datetime(transdat.index).map(lambda x: x.month) # Identify months

transdat["year"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0]) # Identify years

grouped = transdat.groupby(list(set(["year",stick]))) # Group by year and other appropriate variable

plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []}) # Create empty data frame containing what will be plotted

for name, group in grouped:

plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],

"High": max(group.High),

"Low": min(group.Low),

"Close": group.iloc[-1,3]},

index = [group.index[0]]))

if stick == "week": stick = 5

elif stick == "month": stick = 30

elif stick == "year": stick = 365

elif (type(stick) == int and stick >= 1):

transdat["stick"] = [np.floor(i / stick) for i in range(len(transdat.index))]

grouped = transdat.groupby("stick")

plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []}) # Create empty data frame containing what will be plotted

for name, group in grouped:

plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],

"High": max(group.High),

"Low": min(group.Low),

"Close": group.iloc[-1,3]},

index = [group.index[0]]))

else:

raise ValueError('Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer')

# Set plot parameters, including the axis object ax used for plotting

fig, ax = plt.subplots()

fig.subplots_adjust(bottom=0.2)

if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):

weekFormatter = DateFormatter('%b %d') # e.g., Jan 12

ax.xaxis.set_major_locator(mondays)

ax.xaxis.set_minor_locator(alldays)

else:

weekFormatter = DateFormatter('%b %d, %Y')

ax.xaxis.set_major_formatter(weekFormatter)

ax.grid(True)

# Create the candelstick chart

candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat["Open"].tolist(), plotdat["High"].tolist(),

plotdat["Low"].tolist(), plotdat["Close"].tolist())),

colorup = "black", colordown = "red", width = stick * .4)

# Plot other series (such as moving averages) as lines

if otherseries != None:

if type(otherseries) != list:

otherseries = [otherseries]

dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid = True)

ax.xaxis_date()

ax.autoscale_view()

plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

plt.show()

pandas_candlestick_ohlc(apple)

microsoft = web.DataReader("MSFT", "yahoo", start, end)

google = web.DataReader("GOOG", "yahoo", start, end)

# Below I create a DataFrame consisting of the adjusted closing price of these stocks, first by making a list of these objects and using the join method

stocks = pd.DataFrame({"AAPL": apple["Adj Close"],

"MSFT": microsoft["Adj Close"],

"GOOG": google["Adj Close"]})

stocks.head()

# df.apply(arg) will apply the function arg to each column in df, and return a DataFrame with the result

# Recall that lambda x is an anonymous function accepting parameter x; in this case, x will be a pandas Series object

stock_return = stocks.apply(lambda x: x / x[0])

stock_return.head()

# Let's use NumPy's log function, though math's log function would work just as well

import numpy as np

stock_change = stocks.apply(lambda x: np.log(x) - np.log(x.shift(1))) # shift moves dates back by 1.

stock_change.head()

apple["20d"] = np.round(apple["Close"].rolling(window = 20, center = False).mean(), 2)

pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-08-07',:], otherseries = "20d")

start = datetime.datetime(2010,1,1)

apple = web.DataReader("AAPL", "yahoo", start, end)

apple["20d"] = np.round(apple["Close"].rolling(window = 20, center = False).mean(), 2)

pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-08-07',:], otherseries = "20d")

apple["50d"] = np.round(apple["Close"].rolling(window = 50, center = False).mean(), 2)

apple["200d"] = np.round(apple["Close"].rolling(window = 200, center = False).mean(), 2)

pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-08-07',:], otherseries = ["20d", "50d", "200d"])