stockPrediction_unhandle.py

#%%
from pandas_datareader import data, wb
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.plotly as py
from requests_html import HTMLSession
from sklearn.linear_model import LinearRegression


#%%
# df = data.DataReader("gs", 'yahoo', datetime(2008, 1, 1), datetime(2008, 12, 28))
# read the file from online json in Alphavantage
stockCode = '0005.HK'
url = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={0}&outputsize=compact&apikey=TIR873DLX4ZC9WTV'.format(stockCode)
stock_detail = HTMLSession().get(url).json()['Time Series (Daily)']
# df is the original unprocessing dataframe
df = pd.DataFrame.from_dict(stock_detail).T # .T for .transpose()
df.index = pd.to_datetime(df.index,format='%Y-%m-%d') 
df.index.names = ['Date'] # rename index 

moving_average_gruop = [10,20,50]
moving_average_gruop_name = ['Rolling_mean_'+str(i) for i in moving_average_gruop]

# create new processed dataframe and plot graph
df = df.sort_index(ascending=True) 
df.rename(columns={'1. open': 'Open','2. high': 'High','3. low': 'Low','4. close': 'Close','5. volume':'Volume'}, inplace=True) # rename column
df[['Open']] = df[['Open']].astype(float)
df[['High']] = df[['High']].astype(float)
df[['Low']] = df[['Low']].astype(float)
df[['Close']] = df[['Close']].astype(float)
df[['Volume']] = df[['Volume']].astype(int)
for i in range(0,len(moving_average_gruop_name)):
    df[moving_average_gruop_name[i]] = df['Close'].rolling(moving_average_gruop[i]).mean()
df['Date_int'] = pd.to_datetime(df.index)
df['Date_int'] = df['Date_int'].map(datetime.toordinal)

prices = df[['Close']].values
moving_average = []
for i in range(0,len(moving_average_gruop_name)):
    moving_average.append(df[[moving_average_gruop_name[i]]].values)
# print(moving_average)
date_int = df[['Date_int']].values

# print(df)
# split data into 3 parts
prices_1 = prices[10:40]
prices_2 = prices[40:-30]
prices_3 = prices[-30:]
date_int_1 = date_int[10:40]
date_int_2 = date_int[40:-30]
date_int_3 = date_int[-30:]


#%%
regressor_1 = LinearRegression(n_jobs=-1)
regressor_2 = LinearRegression(n_jobs=-1)
regressor_3 = LinearRegression(n_jobs=-1)
regressor_1.fit(date_int_1,prices_1)
regressor_2.fit(date_int_2,prices_2)
regressor_3.fit(date_int_3,prices_3)
print('Regression Done!!')
predict_price_1 = regressor_1.predict(date_int_1)
predict_price_2 = regressor_2.predict(date_int_2)
predict_price_3 = regressor_3.predict(date_int_3)
predict_price_std_1 = predict_price_1.std()
predict_price_std_2 = predict_price_2.std()
predict_price_std_3 = predict_price_3.std()
prices_upper_1,prices_lower_1 =  predict_price_1 + predict_price_std_1*2,predict_price_1 - predict_price_std_1*2
prices_upper_2,prices_lower_2 =  predict_price_2 + predict_price_std_2*2,predict_price_2 - predict_price_std_2*2
prices_upper_3,prices_lower_3 =  predict_price_3 + predict_price_std_3*2,predict_price_3 - predict_price_std_3*2


#%%
INCREASING_COLOR = '#17BECF'
DECREASING_COLOR = '#7F7F7F'
data = [ dict(
    type = 'candlestick',
    open = df.Open,
    high = df.High,
    low = df.Low,
    close = df.Close,
    x = df.index,
    yaxis = 'y2',
    name = stockCode,
    increasing = dict( line = dict( color = INCREASING_COLOR ) ),
    decreasing = dict( line = dict( color = DECREASING_COLOR ) ),
) ]

layout=dict()

fig = dict( data=data, layout=layout )


#%%
fig['layout'] = dict()
fig['layout']['plot_bgcolor'] = 'rgb(250, 250, 250)'
fig['layout']['xaxis'] = dict( rangeselector = dict( visible = True ) )
fig['layout']['yaxis'] = dict( domain = [0, 0.2], showticklabels = False )
fig['layout']['yaxis2'] = dict( domain = [0.2, 0.8] )
fig['layout']['legend'] = dict( orientation = 'h', y=0.9, x=0.3, yanchor='bottom' )
fig['layout']['margin'] = dict( t=40, b=40, r=40, l=40 )
fig['layout']['title'] = 'Moving Linear Regression | Time vs. Price ({0})'.format(stockCode)
fig['layout']['yaxis2']['title'] = 'Cost (HK$)'


#%%
# # Clip the ends
mv_x_1 = df.index[10:]
mv_y_1 = moving_average[0][10:]

fig['data'].append( dict( x=mv_x_1, y=mv_y_1, type='scatter', mode='lines', 
                         line = dict( width = 1 ),
                         marker = dict( color = '#E377C2' ),
                         yaxis = 'y2', name='MA(10)' ) )

mv_x_2 = df.index[20:]
mv_y_2 = moving_average[1][20:]

fig['data'].append( dict( x=mv_x_2, y=mv_y_2, type='scatter', mode='lines', 
                         line = dict( width = 1 ),
                         marker = dict( color = '#A487C2' ),
                         yaxis = 'y2', name='MA(20)' )) 

mv_x_3 = df.index[50:]
mv_y_3 = moving_average[2][50:]

fig['data'].append( dict( x=mv_x_3, y=mv_y_3, type='scatter', mode='lines', 
                         line = dict( width = 1 ),
                         marker = dict( color = '#C597C2' ),
                         yaxis = 'y2', name='MA(50)' )) 


#%%
colors = []

for i in range(len(df.Close)):
    if i != 0:
        if df.Close[i] > df.Close[i-1]:
            colors.append(INCREASING_COLOR)
        else:
            colors.append(DECREASING_COLOR)
    else:
        colors.append(DECREASING_COLOR)


#%%
fig['data'].append( dict( x=df.index, y=df.Volume,                         
                         marker=dict( color=colors ),
                         type='bar', yaxis='y', name='Volume' ) )


#%%
rangeselector=dict(
    visibe = True,
    x = 0, y = 0.9,
    bgcolor = 'rgba(150, 200, 250, 0.4)',
    font = dict( size = 13 ),
    buttons=list([
        dict(count=1,
             label='reset',
             step='all'),
        dict(count=1,
             label='1yr',
             step='year',
             stepmode='backward'),
        dict(count=3,
            label='3 mo',
            step='month',
            stepmode='backward'),
        dict(count=1,
            label='1 mo',
            step='month',
            stepmode='backward'),
        dict(step='all')
    ]))
    
fig['layout']['xaxis']['rangeselector'] = rangeselector


#%%
def bbands(price, window_size=10, num_of_std=2):
    rolling_mean = price.rolling(window=window_size).mean()
    rolling_std  = price.rolling(window=window_size).std()
    upper_band = rolling_mean + (rolling_std*num_of_std)
    lower_band = rolling_mean - (rolling_std*num_of_std)
    return rolling_mean, upper_band, lower_band

bb_avg, bb_upper, bb_lower = bbands(df.Close)

fig['data'].append( dict( x=df.index, y=bb_upper, type='scatter', yaxis='y2', 
                         line = dict( width = 1 ),
                         marker=dict(color='#ccc'), hoverinfo='none', 
                         legendgroup='Bollinger Bands', name='Bollinger Bands') )

fig['data'].append( dict( x=df.index, y=bb_lower, type='scatter', yaxis='y2',
                         line = dict( width = 1 ),
                         marker=dict(color='#ccc'), hoverinfo='none',
                         legendgroup='Bollinger Bands', showlegend=False ) )

fig['data'].append( dict( x=df.index[10:40], y=predict_price_1, type='scatter', yaxis='y2',
                         line = dict( width = 1 ),
                         marker=dict(color='#000'),
                         legendgroup='Linear Regression', name='Linear Regression', ) )
fig['data'].append( dict( x=df.index[40:70], y=predict_price_2, type='scatter', yaxis='y2',
                         line = dict( width = 1 ),
                         marker=dict(color='#000'),
                         legendgroup='Linear Regression', showlegend=False , name='Linear Regression'))
fig['data'].append( dict( x=df.index[70:100], y=predict_price_3, type='scatter', yaxis='y2',
                         line = dict( width = 1 ),
                         marker=dict(color='#000'),
                         legendgroup='Linear Regression', showlegend=False, name='Linear Regression' ))


#%%
py.iplot( fig, filename = 'candlestick-test-3', validate = False )


#%%