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pyplot02

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By noir1458
7 min read
pyplot02

tr_eikon_eod_data 검색하여 데이터 사용

pyplot 2

예제 8_1

test1

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import numpy as np
import pandas as pd
import matplotlib.pylab as plt
import matplotlib.colors as mcolors
import matplotlib.dates as dates

def finance1():
    eikon_path = './tr_eikon_eod_data.csv'
    dt_eikon = {
        'Date':np.object_,
        'AAPL.O':np.float64,
        'MSFT.O':np.float64,
        'INTC.O':np.float64,
        'AMZN.O':np.float64,
        'GS.N':np.float64,
        'SPY':np.float64,
        '.SPX':np.float64,
        '.VIX':np.float64,
        'EUR=':np.float64,
        'XAU=':np.float64,
        'GDX':np.float64,
        'GLD':np.float64
    }
    df_eikon = pd.read_csv(eikon_path,dtype=dt_eikon)#,parse_dates=True)
    #print(df_eikon.head(10))

    #fig, (ax0, ax1) = 
    attributes = df_eikon.columns
    fig,axes = plt.subplots(nrows=2,ncols=1,figsize=(10,8))
    fig.suptitle("AAPL.O and EUR=",fontsize = 15, y=0.95)
    fig.subplots_adjust(hspace=0.5)
    # row 1 plot
    axes[0].plot(attributes[0],attributes[1],data=df_eikon)
    axes[0].set_title("Apple Stock")
    axes[0].xaxis.set_major_locator(dates.YearLocator())
    # row 2 plot
    # axes[1]
    axes[1].plot(attributes[0],attributes[9],data=df_eikon)
    axes[1].set_title("EUR/USD Exchange Rate")
    axes[1].xaxis.set_major_locator(dates.YearLocator())
    axes[1].set_xlabel("Dates")

    plt.show()

    return None

test2

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def finance2():
    eikon_path = './tr_eikon_eod_data.csv'
    dt_eikon = {
        'Date':np.unicode_,
        'AAPL.O':np.float64,
        'MSFT.O':np.float64,
        'INTC.O':np.float64,
        'AMZN.O':np.float64,
        'GS.N':np.float64,
        'SPY':np.float64,
        '.SPX':np.float64,
        '.VIX':np.float64,
        'EUR=':np.float64,
        'XAU=':np.float64,
        'GDX':np.float64,
        'GLD':np.float64
    }
    df_eikon = pd.read_csv(eikon_path,dtype=dt_eikon,index_col=0)#,parse_dates=True)
    df_abs_diff = df_eikon.diff()               # absolute value의 difference
    df_pct_diff = df_eikon.pct_change()         # percentage 의 difference
    #print(df_abs_diff)
    #print(df_pct_diff)

    df_pct_diff_avg = df_pct_diff.mean()
    xvalues = df_pct_diff_avg.index          # x값에 df_pct의 index
    yvalues = df_pct_diff_avg.values         # y값에 value

    fig,axis = plt.subplots(figsize=(10,8))
    fig.suptitle("Average Percentage Difference")

    color_names = list(mcolors.CSS4_COLORS)
    ncolors = len(df_pct_diff_avg)
    #print(color_names)
    #print(mcolors.CSS4_COLORS)
    axis.bar(xvalues,yvalues,
             label=df_pct_diff_avg.index, # label
             color = color_names[6:ncolors]) # bar color
    axis.legend(title='Index')
    plt.show()

    return None

test3 (log returns)

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def test_ch8_2():

    eikon_path = './tr_eikon_eod_data.csv'

    dt_eikon = {

        'Date':np.unicode_,

        'AAPL.O':np.float64,

        'MSFT.O':np.float64,

        'INTC.O':np.float64,

        'AMZN.O':np.float64,

        'GS.N':np.float64,

        'SPY':np.float64,

        '.SPX':np.float64,

        '.VIX':np.float64,

        'EUR=':np.float64,

        'XAU=':np.float64,

        'GDX':np.float64,

        'GLD':np.float64

    }

    df_eikon = pd.read_csv(eikon_path,dtype=dt_eikon,index_col=0)#,parse_dates=True)



    # log returns and cumulative sum

    df_log_rets = np.log(df_eikon/df_eikon.shift(1)) # p_t/p_t-1 (윗 칸으로 밑에칸을 나눈다)

    #위에 칸이 시간상으로 과거, log return을 구하기 위해서

    df_cum_log_rets = df_log_rets.cumsum()

    #print(df_cum_log_rets)



    #ploting

    fig,axis = plt.subplots(figsize=(10,8))

    xvalues = df_cum_log_rets.dropna().index

    attributes = df_cum_log_rets.columns # attribute[0] = "AAPL.0" => 'GLD'



    #print(xvalues,attributes)

    axis.set_title("Cumulative Log Returns",fontsize=15)

    axis.set_xlabel("Dates",fontsize=15)

    axis.xaxis.set_major_locator(dates.YearLocator())

    for attribute in attributes:

        yvalues = df_cum_log_rets[attribute].dropna().values

        axis.plot(xvalues,yvalues,label=attribute)

    axis.legend()

    #plt.reParams["axes.unicode_minus"] = False

    plt.show()

    return None

test4 (Rolling Statistics)

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def rol_statics1():
    eikon_path = './tr_eikon_eod_data.csv'
    dt_eikon = {
        'Date':np.object_,
        'AAPL.O':np.float64,
        'MSFT.O':np.float64,
        'INTC.O':np.float64,
        'AMZN.O':np.float64,
        'GS.N':np.float64,
        'SPY':np.float64,
        '.SPX':np.float64,
        '.VIX':np.float64,
        'EUR=':np.float64,
        'XAU=':np.float64,
        'GDX':np.float64,
        'GLD':np.float64
    }
    df_eikon = pd.read_csv(eikon_path,dtype=dt_eikon)#,parse_dates=True)
    
    attributes = df_eikon.columns
    wsize = 20
    apple = attributes[1]

    #df_apple = df_eikon.loc[apple]#.dropna()
    df_eikon['mean'] = df_eikon[apple].rolling(window=wsize).mean()
    df_eikon["median"] = df_eikon[apple].rolling(window=wsize).median()
    df_eikon["max"] = df_eikon[apple].rolling(window=wsize).max()
    
    
    fig,axis = plt.subplots(figsize=(10,8))
    axis.set_title('Rolling Statistics of Tail 200 Rows',fontsize=15)
    axis.set_xlabel('Dates',fontsize=15)

    xvalues = df_eikon.index
    axis.xaxis.set_major_locator(dates.MonthLocator())
    yvalues = df_eikon[apple].values
    axis.plot(xvalues[-200:],yvalues[-200:],label="Original")
    yvalues = df_eikon['mean'].values
    axis.plot(xvalues[-200:],yvalues[-200:],'tab:red',linestyle="dashed",label="Rolling mean")
    yvalues = df_eikon["median"].values
    axis.plot(xvalues[-200:],yvalues[-200:],"tab:orange",linestyle="dashed",label = "SMA short")
    yvalues = df_eikon['max'].values
    axis.plot(xvalues[-200:],yvalues[-200:],"tab:olive",linestyle="dashed",label = "SMA Long")
    axis.legend(title = "Markers")
    plt.show()
    
    
    return None

test5

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def rol_statics2():
    eikon_path = './tr_eikon_eod_data.csv'
    dt_eikon = {
        'Date':np.object_,
        'AAPL.O':np.float64,
        'MSFT.O':np.float64,
        'INTC.O':np.float64,
        'AMZN.O':np.float64,
        'GS.N':np.float64,
        'SPY':np.float64,
        '.SPX':np.float64,
        '.VIX':np.float64,
        'EUR=':np.float64,
        'XAU=':np.float64,
        'GDX':np.float64,
        'GLD':np.float64
    }
    df_eikon = pd.read_csv(eikon_path,dtype=dt_eikon)#,parse_dates=True)
    #print(df_eikon)

    
    apple = 'AAPL.O'
    
    
    df_eikon['SMA.short'] = df_eikon[apple].rolling(window=42).mean()
    df_eikon["SMA.long"] = df_eikon[apple].rolling(window=252).mean()
    
    
    fig,axis = plt.subplots(figsize=(10,8))
    axis.set_title('Technical Analysis : AAPL.O by SMAs',fontsize=15)
    axis.set_xlabel('Dates',fontsize=15)
    xvalues = df_eikon.index

    axis.xaxis.set_major_locator(dates.YearLocator())
    yvalues = df_eikon[apple].values
    axis.plot(xvalues,yvalues,'tab:blue',label="Original")

    yvalues = df_eikon["SMA.short"].values
    axis.plot(xvalues,yvalues,"tab:orange",label = "SMA short")

    yvalues = df_eikon['SMA.long'].values
    axis.plot(xvalues,yvalues,"tab:brown",label = "SMA Long")
    axis.legend(title = "Markers")

    df_eikon['positions'] = np.where(df_eikon['SMA.short']>df_eikon['SMA.long'],1,-1)
    axis2 = axis.twinx() #another y axis
    yvalues = df_eikon['positions'].values
    axis2.plot(xvalues,yvalues,"tab:cyan")
    #plt.rcParams["axes.unicode_minus"] = False # 우측에 마이너스 나타나면서 깨질 경우

    plt.show()
    print(df_eikon)
    
    return None


def main():
    #finance1()
    #finance2()
    #test_ch8_2()
    #rol_statics1()
    rol_statics2()
    return None
if __name__ == '__main__':
    main()

np.where 사용하여 short이 클때는 1 아닐때는 -1 의 내용을 가진 열을 하나 더 만들고, 새로운 y축 axis2를 만들어서 하늘색 선을 그린다.

numpy_pandas
numpy_pandas
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