Getting Open, High, Low, Close for 5 min stock data python

问题

I have a DataFrame that contains stock data with the following columns:

            time    ticker  price
0   2020-04-02 09:30:35 EV  33.860
1   2020-04-02 09:00:00 AMG 60.430
2   2020-04-02 09:30:35 AMG 60.750
3   2020-04-02 09:00:00 BLK 455.350
4   2020-04-02 09:30:35 BLK 451.514
... ... ... ...
502596  2020-04-02 13:00:56 TLT 166.450
502597  2020-04-02 13:00:56 VXX 47.150
502598  2020-04-02 13:00:56 TSLA 529.800
502599  2020-04-02 13:00:56 BIDU 103.500
502600  2020-04-02 13:00:56 ON  12.700

Its stock price data for every 20 seconds for several hundred stocks. I have put the time column into datetime format.

From here, I need to separate the data into 5 minute intervals then plot the data, which I've done by using:

out = df.groupby("ticker")\
        .apply(lambda x: x.set_index("time")\
                          .resample("5T")\
                          .first()\
                          .reset_index())\
        .reset_index(drop=True)

dffinal = out.dropna(axis=0)

def plot_tick(data, ticker):
    ts = data[data["ticker"]==ticker].reset_index(drop=True)
    ts.plot(x="time", y="price",title=ticker,figsize=(20,20),kind='line')

plot_tick(dffinal, "A")

The chart comes out well, but the problem is I need the high ,low, open, and close prices for each 5 minute interval for each stock. I need this in order to make a candlestick chart, which I can easily do once I have high, low,open, and close columns.

The open and close is the price at the beginning and end of the 5 minute period, respectively. The high and low column is the highest price during the interval, and lowest price during the interval, respectively.

So I'm looking for and end result like this:

         time          ticker   price        open close high low
0   2020-04-02 09:00:00 A   72.6700
6   2020-04-02 09:30:00 A   72.1400
7   2020-04-02 09:35:00 A   72.5400
8   2020-04-02 09:40:00 A   72.4000
9   2020-04-02 09:45:00 A   72.3338
... ... ... ...
38895   2020-04-02 12:40:00 ZUMZ    17.6000
38896   2020-04-02 12:45:00 ZUMZ    17.6300
38897   2020-04-02 12:50:00 ZUMZ    17.6000
38898   2020-04-02 12:55:00 ZUMZ    17.7400
38899   2020-04-02 13:00:00 ZUMZ    17.560

with the open, close,high,low filled in, obviously.

回答1:

IIUC, in the groupby you can do it by 'ticker' but also using pd.Grouper on 'time' with a frequence of 5 minutes. In the agg method, you can use since pandas>0.25 pd.NamedAgg with first for open, last for close, max for high and min for low.

# dummy variables
np.random.seed(0)
df = pd.DataFrame({'time':pd.date_range('2020-04-01 9:30:00', freq='20s', periods=50).tolist()*2, 
                   'ticker': ['ticker1']*50 + ['ticker2']*50, 
                   'price':np.random.randint(30, 50, 100)})

# groupby and agg, then reset_index
df_f = df.groupby(['ticker', pd.Grouper(key='time', freq='5T')])\
         .agg(open=pd.NamedAgg(column='price', aggfunc='first'), 
              close=pd.NamedAgg(column='price', aggfunc='last'), 
              high=pd.NamedAgg(column='price', aggfunc='max'), 
              low=pd.NamedAgg(column='price', aggfunc='min'))\
         .reset_index()

print (df_f)
    ticker                time  open  close  high  low
0  ticker1 2020-04-01 09:30:00    42     37    49   30
1  ticker1 2020-04-01 09:35:00    44     33    49   30
2  ticker1 2020-04-01 09:40:00    47     32    49   30
3  ticker1 2020-04-01 09:45:00    30     36    36   30
4  ticker2 2020-04-01 09:30:00    38     48    48   31
5  ticker2 2020-04-01 09:35:00    30     44    45   30
6  ticker2 2020-04-01 09:40:00    45     34    48   30
7  ticker2 2020-04-01 09:45:00    32     40    46   32

for plotting candlestick with plotly, you can do:

import plotly.figure_factory

def plot_tick(data, ticker):
    ts = data[data["ticker"]==ticker].reset_index(drop=True)
    fig = plotly.figure_factory.create_candlestick(ts.open, ts.high, ts.low, 
                                                   ts.close, dates=ts.time)
    fig.show()

plot_tick(df_f, 'ticker1')

来源：https://stackoverflow.com/questions/60999601/getting-open-high-low-close-for-5-min-stock-data-python

标签

python

pandas

dataframe

finance

stock