all project files

all_model.py

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+import pickle
+import numpy as np
+import xgboost
+# from xgboost import XGBClassifier
+# import xgboost as xgb
+
+"""Input: NULL
+   Output: Model
+"""
+def load_model():
+    load_model = pickle.load(open('model/xgb_f_beta_model.sav','rb'))
+    
+    return load_model
+
+
+""" Input: Model, Selected_date Data
+    Output: Predicted Score
+"""
+def prediction(model,data):
+    pred = model.predict_proba(data)
+    score = np.average(pred[:,1:])
+    
+    return score

app.py

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+import streamlit as st
+import pandas as pd
+import datetime
+import numpy as np
+import datetime
+import helper
+import all_model
+
+def show_information():
+    # Show Information about the selected Stock
+    st.header('🤫Did you know💡')
+    st.caption("Analyzing data from 2015 to 2021")
+    st.text("1) There is a 60% chance of gap up opening in any random trade in Reliance 😮 ")
+    st.text("2) 1% of the gap up is more than Rs:15.00 i.e more quantity == more profit😇")
+    st.text("3) Median, Q3 or 75th percentile have increased from 2015(1.8) to 2021(11.55)💰")
+
+def select_date():
+    # Select the date for Prediction
+    selected_date = st.date_input(
+        "Which date you want to check",
+        datetime.date(2022, 3, 6))
+    st.write('Your selected date is:', selected_date)
+    
+    return selected_date
+
+@st.cache
+def prepare_data_for_selected_date():
+    df = pd.read_csv("dataset/reliance_30min.csv")
+    df = helper.format_date(df)
+    df = helper.replace_vol(df)
+    df = helper.feature_main(df) 
+    
+    return df
+
+def freature_data(df,date):
+    # st.dataframe(df.loc[str(date)])
+    df = df.loc[str(date)]
+    df = df.drop(columns=['date'],axis=1)
+    
+    
+    return df
+
+
+def show_prediction_result(prepared_data):
+    model = all_model.load_model()
+    result = all_model.prediction(model,prepared_data)
+    
+    return result
+    
+
+
+def main():
+    st.title('PROFIT IN THE MORNING!')
+    option = st.selectbox(
+        'Which stock would you like to analyze?',
+        ('None','Reliance', 'Airtel', 'State Bank Of India'))
+
+    st.write('You selected:', option)
+
+
+
+    if option=="Reliance":
+        data_link = ("C:/Users/Rajdeep Borgohain.000/Desktop/reliance_30min.csv")
+        dateSelect = False
+        # About Reliance Stock
+        show_information()
+        selected_date = select_date()
+        prepared_data = prepare_data_for_selected_date()
+        prepared_data = freature_data(prepared_data,selected_date)
+        score = show_prediction_result(prepared_data)
+        st.write('')
+        selected_date+=datetime.timedelta(days=1)
+        
+        if score == 'nan':
+            text = f'No data avaliable for the selected date {selected_date}'
+            st.warning(text)
+        elif score >= 0.5:
+            score = np.round(score,4)*100
+            text = f'The chances of Gap up on:      {selected_date}     is      {score}%'
+            st.success(text)
+        elif score < 0.5:
+            text = f'The chances of Gap up on: {selected_date}  is  {score}'
+            st.error(text)
+    else:
+        st.text('Data Not Avaliable!')
+        
+if __name__ == "__main__":
+    main()

helper.py

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+import pandas as pd
+import talib as ta
+import numpy as np
+
+def format_date(df):
+    format = '%Y-%m-%d %H:%M:%S'
+    df['Datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'], format=format)
+    df = df.set_index(pd.DatetimeIndex(df['Datetime']))
+    df = df.drop('Datetime', axis=1)
+    
+    return df
+
+# https://stackoverflow.com/questions/39684548/convert-the-string-2-90k-to-2900-or-5-2m-to-5200000-in-pandas-dataframe
+def replace_vol(df):
+    df.volume = (df.volume.replace(r'[KM]+$', '', regex=True).astype(float) * \
+                df.volume.str.extract(r'[\d\.]+([KM]+)', expand=False)
+                .fillna(1)
+                .replace(['K','M'], [10**3, 10**6]).astype(int))
+    return df
+
+def get_all_features(df):
+    #get_overlap_studies
+    # BBANDS - Bollinger Bands
+    df['bbub'], df['bbmb'], df['bblb'] = ta.BBANDS(df['close'])
+
+    # DEMA - Double Exponential Moving Average
+    df['DEMA_100'] = ta.DEMA(df['close'],timeperiod=100)
+    df['DEMA_30'] = ta.DEMA(df['close'],timeperiod=30)
+    df['DEMA_5'] = ta.DEMA(df['close'],timeperiod=5)
+
+    # EMA - Exponential Moving Average
+    df['EMA_100'] = ta.EMA(df['close'],timeperiod=100)
+    df['EMA_30'] = ta.EMA(df['close'],timeperiod=30)
+    df['EMA_5'] = ta.EMA(df['close'],timeperiod=5)
+
+    # HT_TRENDLINE - Hilbert Transform - Instantaneous Trendline
+    df['HT_TRENDLINE'] = ta.HT_TRENDLINE(df['close'])
+
+    # KAMA - Kaufman Adaptive Moving Average
+    df['KAMA'] = ta.KAMA(df['close'])
+
+    # MA - Moving average
+    df['MA_100'] = ta.MA(df['close'],timeperiod=100)
+    df['MA_30'] = ta.MA(df['close'],timeperiod=30)
+    df['MA_5'] = ta.MA(df['close'],timeperiod=5)
+
+    # MAMA - MESA Adaptive Moving Average
+    df['MAMA'], df['FAMA'] = ta.MAMA(df['close'])
+
+    # MIDPOINT - MidPoint over period
+    df['MIDPOINT'] = ta.MIDPOINT(df['close'])
+
+    # MIDPRICE - Midpoint Price over period
+    df['MIDPRICE'] = ta.MIDPRICE(df.high, df.low, timeperiod=14)
+
+    # SAR - Parabolic SAR
+    df['SAR'] = ta.SAR(df.high, df.low, acceleration=0, maximum=0)
+
+    # SAREXT - Parabolic SAR - Extended
+    df['SAREXT'] = ta.SAREXT(df.high, df.low, startvalue=0, offsetonreverse=0, accelerationinitlong=0, accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0)
+
+    # SMA - Simple Moving Average
+    df['SMA_100'] = ta.SMA(df['close'],timeperiod=100)
+    df['SMA_30'] = ta.SMA(df['close'],timeperiod=30)
+    df['SMA_5'] = ta.SMA(df['close'],timeperiod=5)
+
+    # T3 - Triple Exponential Moving Average (T3)
+    df['T3'] = ta.T3(df.close, timeperiod=5, vfactor=0)
+
+    # TEMA - Triple Exponential Moving Average
+    df['TEMA_100'] = ta.TEMA(df['close'],timeperiod=100)
+    df['TEMA_30'] = ta.TEMA(df['close'],timeperiod=30)
+    df['TEMA_5'] = ta.TEMA(df['close'],timeperiod=5)
+
+    # TRIMA - Triangular Moving Average
+    df['TRIMA_100'] = ta.TRIMA(df['close'],timeperiod=100)
+    df['TRIMA_30'] = ta.TRIMA(df['close'],timeperiod=30)
+    df['TRIMA_5'] = ta.TRIMA(df['close'],timeperiod=5)
+
+    # WMA - Weighted Moving Average
+    df['WMA_100'] = ta.WMA(df['close'],timeperiod=100)
+    df['WMA_30'] = ta.WMA(df['close'],timeperiod=30)
+    df['WMA_5'] = ta.WMA(df['close'],timeperiod=5)
+
+
+    #get_momentum_indicator
+    # ADX - Average Directional Movement Index
+    df['ADX'] =  ta.ADX(df.high, df.low, df.close, timeperiod=14)
+
+    # ADXR - Average Directional Movement Index Rating
+    df['ADXR'] = ta.ADXR(df.high, df.low, df.close, timeperiod=14)
+
+    # APO - Absolute Price Oscillator
+    df['APO'] = ta.APO(df.close, fastperiod=12, slowperiod=26, matype=0)
+
+    # AROON - Aroon
+    df['AROON_DWN'],df['AROON_UP'] = ta.AROON(df.high, df.low, timeperiod=14)
+
+    # AROONOSC - Aroon Oscillator
+    df['AROONOSC'] = ta.AROONOSC(df.high, df.low, timeperiod=14)
+
+    # BOP - Balance Of Power
+    df['BOP'] = ta.BOP(df.open, df.high, df.low, df.close)
+
+    # CCI - Commodity Channel Index
+    df['CCI'] = ta.CCI(df.high, df.low, df.close, timeperiod=14)
+
+    # CMO - Chande Momentum Oscillator
+    df['CMO']= ta.CMO(df.close, timeperiod=14)
+
+    # DX - Directional Movement Index
+    df['DX'] = ta.DX(df.high, df.low, df.close, timeperiod=14)
+
+    # MACD - Moving Average Convergence/Divergence
+    df['MACD'], df['MACD_SGNL'], df['MACD_HIST'] = ta.MACD(df.close, fastperiod=12, slowperiod=26, signalperiod=9)
+
+    # MACDFIX - Moving Average Convergence/Divergence Fix 12/26
+    df['MACDF'], df['MACDF_SGNL'], df['MACDF_HIST'] = ta.MACDFIX(df.close)
+
+    # MFI - Money Flow Index
+    df['MFI'] = ta.MFI(df.high, df.low, df.close, df.volume, timeperiod=14)
+
+    # MINUS_DI - Minus Directional Indicator
+    df['MINUS_DI'] = ta.MINUS_DI(df.high, df.low, df.close, timeperiod=14)
+
+    # MINUS_DM - Minus Directional Movement
+    df['MINUS_DM'] = ta.MINUS_DM(df.high, df.low, timeperiod=14)
+
+    # MOM - Momentum
+    df['MOM'] = ta.MOM(df.close, timeperiod=10)
+
+    # PLUS_DI - Plus Directional Indicator
+    df['PLUS_DI'] = ta.PLUS_DI(df.high, df.low, df.close, timeperiod=14)
+
+    # PLUS_DM - Plus Directional Indicator
+    df['PLUS_DM'] = ta.PLUS_DM(df.high, df.low, timeperiod=14)
+
+    # PPO - Percentage Price Oscillator
+    df['PPO'] = ta.PPO(df.close, fastperiod=12, slowperiod=26, matype=0)
+
+    # ROC - Rate of change : ((price/prevPrice)-1)*100
+    df['ROC'] = ta.ROC(df.close, timeperiod=10)
+
+    # ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice
+    df['ROCP'] = ta.ROCP(df.close, timeperiod=10)
+
+    # ROCR - Rate of change Percentage: (price-prevPrice)/prevPrice
+    df['ROCR'] = ta.ROCR(df.close, timeperiod=10)
+
+    # ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
+    df['ROCR100'] = ta.ROCR100(df.close, timeperiod=10)
+
+    # RSI - Relative Strength Index
+    df['RSI'] = ta.RSI(df.close, timeperiod=14)
+
+    # STOCH - Stochastic
+    df['STOCH_SLWK'], df['STOCH_SLWD'] = ta.STOCH(df.high, df.low, df.close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
+
+    # STOCHF - Stochastic Fast
+    df['STOCH_FSTK'], df['STOCH_FSTD'] = ta.STOCHF(df.high, df.low, df.close, fastk_period=5, fastd_period=3, fastd_matype=0)
+
+    # STOCHRSI - Stochastic Relative Strength Index
+    df['STOCHRSI_FSTK'], df['STOCHRSI_FSTD'] = ta.STOCHRSI(df.close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
+
+    # TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
+    df['TRIX'] = ta.TRIX(df.close, timeperiod=30)
+
+    # ULTOSC - Ultimate Oscillator
+    df['ULTOSC'] = ta.ULTOSC(df.high, df.low, df.close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
+
+    # WILLR - Williams' %R
+    df['WILLR'] = ta.WILLR(df.high, df.low, df.close, timeperiod=14)
+
+
+    # get_volume_indicator
+    # AD - Chaikin A/D Line
+    df['AD'] = ta.AD(df.high, df.low, df.close, df.volume)
+
+    # ADOSC - Chaikin A/D Oscillator
+    df['ADOSC'] = ta.ADOSC(df.high, df.low, df.close, df.volume, fastperiod=3, slowperiod=10)
+
+    # OBV - On Balance Volume
+    df['OBV'] = ta.OBV(df.close, df.volume)
+
+
+    # get_volatility_indicator
+    # ATR - Average True Range
+    df['ATR'] = ta.ATR(df.high, df.low, df.close, timeperiod=14)
+
+    # NATR - Normalized Average True Range
+    df['NATR'] = ta.NATR(df.high, df.low, df.close, timeperiod=14)
+
+    # TRANGE - True Range
+    df['TRANGE'] = ta.TRANGE(df.high, df.low, df.close)
+
+
+    # get_transform_price
+    # AVGPRICE - Average Price
+    df['AVGPRICE'] = ta.AVGPRICE(df.open, df.high, df.low, df.close)
+
+    # MEDPRICE - Median Price
+    df['MEDPRICE'] = ta.MEDPRICE(df.high, df.low)
+
+    # TYPPRICE - Typical Price
+    df['TYPPRICE'] = ta.TYPPRICE(df.high, df.low, df.close)
+
+    # WCLPRICE - Weighted Close Price
+    df['WCLPRICE'] = ta.WCLPRICE(df.high, df.low, df.close)
+
+
+    # get_cycle_indicator
+    # HT_DCPERIOD - Hilbert Transform - Dominant Cycle Period
+    df['HT_DCPERIOD'] = ta.HT_DCPERIOD(df.close)
+
+    # HT_DCPHASE - Hilbert Transform - Dominant Cycle Phase
+    df['HT_DCPHASE'] = ta.HT_DCPHASE(df.close)
+
+    # HT_PHASOR - Hilbert Transform - Phasor Components
+    df['HT_PHASOR_IP'], df['HT_PHASOR_QD'] = ta.HT_PHASOR(df.close)
+
+    # HT_SINE - Hilbert Transform - SineWave
+    df['HT_SINE'], df['HT_SINE_LEADSINE'] = ta.HT_SINE(df.close)
+
+    # HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode
+    df['HT_TRENDMODE'] = ta.HT_TRENDMODE(df.close)
+
+    return df
+
+def feature_main(df):
+    df['time'] = df['time'].map(lambda x: np.sum(list(map(int, str(x).split(':')))))
+
+    df = get_all_features(df)
+    values = {}
+    for col in df.columns:
+        idx = df.reset_index()[col].first_valid_index()
+        values[col] = df.iloc[idx][col]
+    df = df.fillna(value=values)
+    return df