making things faster

app.py

@@ -4,9 +4,10 @@ import numpy as np
 from sklearn.metrics import roc_auc_score, precision_score, recall_score
 from pandas.tseries.offsets import BDay
 import streamlit as st
-from datetime import datetime, time, timedelta
+from datetime import datetime
 import pytz
 import holidays
+from getDailyData import get_daily
 
 st.set_page_config(
     page_title="Gameday $SPX",
@@ -121,12 +122,12 @@ with st.form("choose_model"):
         
         if option == '06:30':
 
-            from model_day import *
+            from model_day_v2 import *
 
             fname='performance_for_open_model.csv'
 
             my_bar.progress(0.33, 'Loading data...')
-            data, df_final, final_row = get_data()
+            data, df_final, final_row = get_daily()
             # st.success("✅ Historical data")
 
             my_bar.progress(0.66, "Training models...")
@@ -187,11 +188,11 @@ with st.form("choose_model"):
         
         else:
 
-            from model_intra import *
+            from model_intra_v2 import *
             idx = times_list.index(option)
 
             my_bar.progress(0.33, 'Loading data...')
-            data, df_final, final_row = get_data(idx)
+            data, df_final, final_row = get_daily(mode='intra', periods_30m=idx)
             # st.success("✅ Historical data")
 
             my_bar.progress(0.66, "Training models...")

dailyCols.py

@@ -0,0 +1,33 @@
+model_cols = [
+    'BigNewsDay',
+    'Quarter',
+    'Perf5Day',
+    'Perf5Day_n1',
+    'DaysGreen',
+    'DaysRed',
+    'CurrentGap',
+    'RangePct',
+    'RangePct_n1',
+    'RangePct_n2',
+    'OHLC4_VIX',
+    'OHLC4_VIX_n1',
+    'OHLC4_VIX_n2',
+    'VIXOpen',
+    'VVIXOpen',
+    'OpenL1',
+    'OpenL2',
+    'OpenH1',
+    'OpenH2',
+    'L1TouchPct',
+    'L2TouchPct',
+    'H1TouchPct',
+    'H2TouchPct',
+    'L1BreakPct',
+    'L2BreakPct',
+    'H1BreakPct',
+    'H2BreakPct',
+    'H1BreakTouchPct',
+    'H2BreakTouchPct',
+    'L1BreakTouchPct',
+    'L2BreakTouchPct'
+]

getDailyData.py

@@ -0,0 +1,278 @@
+import pandas as pd
+import pandas_datareader as pdr
+import numpy as np
+import yfinance as yf
+import requests
+from bs4 import BeautifulSoup
+from typing import List
+from tqdm import tqdm
+import os
+import datetime
+import json
+from sqlalchemy import create_engine
+
+data_start_date = '2018-07-01'
+
+def get_daily(mode='daily', periods_30m=None):
+    '''
+    Method to get daily data and create daily features. Optionally append intra data if specified.
+    `mode`: 'daily' or 'intra'.
+    `periods_30m`: How many 30m periods to bring in. Only specify if mode == 'intra'.
+    '''
+    
+    vix = yf.Ticker('^VIX')
+    vvix = yf.Ticker('^VVIX')
+    spx = yf.Ticker('^GSPC')
+
+    prices_vix = vix.history(start=data_start_date, interval='1d')
+    prices_vvix = vvix.history(start=data_start_date, interval='1d')
+    prices_spx = spx.history(start=data_start_date, interval='1d')
+
+    prices_spx['index'] = [str(x).split()[0] for x in prices_spx.index]
+    prices_spx['index'] = pd.to_datetime(prices_spx['index']).dt.date
+    prices_spx.index = prices_spx['index']
+    prices_spx = prices_spx.drop(columns='index')
+    prices_spx.index = pd.DatetimeIndex(prices_spx.index)
+
+    prices_vix['index'] = [str(x).split()[0] for x in prices_vix.index]
+    prices_vix['index'] = pd.to_datetime(prices_vix['index']).dt.date
+    prices_vix.index = prices_vix['index']
+    prices_vix = prices_vix.drop(columns='index')
+    prices_vix.index = pd.DatetimeIndex(prices_vix.index)
+
+    prices_vvix['index'] = [str(x).split()[0] for x in prices_vvix.index]
+    prices_vvix['index'] = pd.to_datetime(prices_vvix['index']).dt.date
+    prices_vvix.index = prices_vvix['index']
+    prices_vvix = prices_vvix.drop(columns='index')
+    prices_vvix.index = pd.DatetimeIndex(prices_vvix.index)
+    
+    if mode == 'intra':
+        from getIntraData import get_intra
+        df_intra = get_intra(periods_30m)
+        data = prices_spx.merge(df_intra, left_index=True, right_index=True)
+    else:
+        data = prices_spx.copy()
+
+    data = data.merge(prices_vix[['Open','High','Low','Close']], left_index=True, right_index=True, suffixes=['','_VIX'])
+    data = data.merge(prices_vvix[['Open','High','Low','Close']], left_index=True, right_index=True, suffixes=['','_VVIX'])
+
+    # Features
+    data['PrevClose'] = data['Close'].shift(1)
+    data['Perf5Day'] = data['Close'] > data['Close'].shift(5)
+    data['Perf5Day_n1'] = data['Perf5Day'].shift(1)
+    data['Perf5Day_n1'] = data['Perf5Day_n1'].astype(bool)
+    data['GreenDay'] = (data['Close'] > data['PrevClose']) * 1
+    data['RedDay'] = (data['Close'] <= data['PrevClose']) * 1
+
+    data['VIX5Day'] = data['Close_VIX'] > data['Close_VIX'].shift(5)
+    data['VIX5Day_n1'] = data['VIX5Day'].astype(bool)
+
+    data['VVIX5Day'] = data['Close_VVIX'] > data['Close_VVIX'].shift(5)
+    data['VVIX5Day_n1'] = data['VVIX5Day'].astype(bool)
+
+    data['VIXOpen'] = data['Open_VIX'] > data['Close_VIX'].shift(1)
+    data['VVIXOpen'] = data['Open_VVIX'] > data['Close_VVIX'].shift(1)
+    data['VIXOpen'] = data['VIXOpen'].astype(bool)
+    data['VVIXOpen'] = data['VVIXOpen'].astype(bool)
+
+    data['Range'] = data[['Open','High']].max(axis=1) - data[['Low','Open']].min(axis=1) # Current day range in points
+    data['RangePct'] = data['Range'] / data['Close']
+    data['VIXLevel'] = pd.qcut(data['Close_VIX'], 4)
+    data['OHLC4_VIX'] = data[['Open_VIX','High_VIX','Low_VIX','Close_VIX']].mean(axis=1)
+    data['OHLC4'] = data[['Open','High','Low','Close']].mean(axis=1)
+    data['OHLC4_Trend'] = data['OHLC4'] > data['OHLC4'].shift(1)
+    data['OHLC4_Trend'] = data['OHLC4_Trend'].astype(bool)
+    data['OHLC4_Trend_n1'] = data['OHLC4_Trend'].shift(1)
+    data['OHLC4_Trend_n1'] = data['OHLC4_Trend_n1'].astype(float)
+    data['OHLC4_Trend_n2'] = data['OHLC4_Trend'].shift(1)
+    data['OHLC4_Trend_n2'] = data['OHLC4_Trend_n2'].astype(float)
+    data['RangePct_n1'] = data['RangePct'].shift(1)
+    data['RangePct_n2'] = data['RangePct'].shift(2)
+    data['OHLC4_VIX_n1'] = data['OHLC4_VIX'].shift(1)
+    data['OHLC4_VIX_n2'] = data['OHLC4_VIX'].shift(2)
+    data['CurrentGap'] = (data['Open'] - data['PrevClose']) / data['PrevClose']
+    data['CurrentGapHist'] = data['CurrentGap'].copy()
+    data['CurrentGap'] = data['CurrentGap'].shift(-1)
+    data['DayOfWeek'] = pd.to_datetime(data.index)
+    data['DayOfWeek'] = data['DayOfWeek'].dt.day
+
+    # Target -- the next day's low
+    data['Target'] = (data['OHLC4'] / data['PrevClose']) - 1
+    data['Target'] = data['Target'].shift(-1)
+    # data['Target'] = data['RangePct'].shift(-1)
+
+    # Target for clf -- whether tomorrow will close above or below today's close
+    data['Target_clf'] = data['Close'] > data['PrevClose']
+    data['Target_clf'] = data['Target_clf'].shift(-1)
+    data['DayOfWeek'] = pd.to_datetime(data.index)
+    data['Quarter'] = data['DayOfWeek'].dt.quarter
+    data['DayOfWeek'] = data['DayOfWeek'].dt.weekday
+
+    # Calculate up
+    data['up'] = 100 * (data['High'].shift(1) - data['Open'].shift(1)) / data['Close'].shift(1)
+
+    # Calculate upSD
+    data['upSD'] = data['up'].rolling(30).std(ddof=0)
+
+    # Calculate aveUp
+    data['aveUp'] = data['up'].rolling(30).mean()
+    data['H1'] = data['Open'] + (data['aveUp'] / 100) * data['Open']
+    data['H2'] = data['Open'] + ((data['aveUp'] + data['upSD']) / 100) * data['Open']
+    data['down'] = 100 * (data['Open'].shift(1) - data['Low'].shift(1)) / data['Close'].shift(1)
+    data['downSD'] = data['down'].rolling(30).std(ddof=0)
+    data['aveDown'] = data['down'].rolling(30).mean()
+    data['L1'] = data['Open'] - (data['aveDown'] / 100) * data['Open']
+    data['L2'] = data['Open'] - ((data['aveDown'] + data['downSD']) / 100) * data['Open']
+
+    data = data.assign(
+        L1Touch = lambda x: x['Low'] < x['L1'],
+        L2Touch = lambda x: x['Low'] < x['L2'],
+        H1Touch = lambda x: x['High'] > x['H1'],
+        H2Touch = lambda x: x['High'] > x['H2'],
+        L1Break = lambda x: x['Close'] < x['L1'],
+        L1TouchRed = lambda x: (x['Low'] < x['L2']) & (x['Close'] < x['PrevClose']),
+        L2TouchL1Break = lambda x: (x['Low'] < x['L2']) & (x['Close'] < x['L1']),
+        L2Break = lambda x: x['Close'] < x['L2'],
+        H1Break = lambda x: x['Close'] > x['H1'],
+        H1TouchGreen = lambda x: (x['High'] > x['H1']) & (x['Close'] > x['PrevClose']),
+        H2TouchH1Break = lambda x: (x['High'] > x['H2']) & (x['Close'] > x['H1']),
+        H2Break = lambda x: x['Close'] > x['H2'],
+        OpenL1 = lambda x: np.where(x['Open'] < x['L1'], 1, 0),
+        OpenL2 = lambda x: np.where(x['Open'] < x['L2'], 1, 0),
+        OpenH1 = lambda x: np.where(x['Open'] > x['H1'], 1, 0),
+        OpenH2 = lambda x: np.where(x['Open'] > x['H2'], 1, 0)
+    )
+
+    data['OpenL1'] = data['OpenL1'].shift(-1)
+    data['OpenL2'] = data['OpenL2'].shift(-1)
+    data['OpenH1'] = data['OpenH1'].shift(-1)
+    data['OpenH2'] = data['OpenH2'].shift(-1)
+
+
+    level_cols = [
+        'L1Touch',
+        'L2Touch',
+        'H1Touch',
+        'H2Touch',
+        'L1Break',
+        'L2Break',
+        'H1Break',
+        'H2Break'
+    ]
+
+    for col in level_cols:
+        data[col+'Pct'] = data[col].rolling(100).mean()
+        # data[col+'Pct'] = data[col+'Pct'].shift(-1)
+
+    data['H1BreakTouchPct'] = data['H1Break'].rolling(100).sum() / data['H1Touch'].rolling(100).sum()
+    data['H2BreakTouchPct'] = data['H2Break'].rolling(100).sum() / data['H2Touch'].rolling(100).sum()
+    data['L1BreakTouchPct'] = data['L1Break'].rolling(100).sum() / data['L1Touch'].rolling(100).sum()
+    data['L2BreakTouchPct'] = data['L2Break'].rolling(100).sum() / data['L2Touch'].rolling(100).sum()
+    data['L1TouchRedPct'] = data['L1TouchRed'].rolling(100).sum() / data['L1Touch'].rolling(100).sum()
+    data['H1TouchGreenPct'] = data['H1TouchGreen'].rolling(100).sum() / data['H1Touch'].rolling(100).sum()
+
+    data['H1BreakH2TouchPct'] = data['H2TouchH1Break'].rolling(100).sum() / data['H2Touch'].rolling(100).sum()
+    data['L1BreakL2TouchPct'] = data['L2TouchL1Break'].rolling(100).sum() / data['L2Touch'].rolling(100).sum()
+
+    if mode=='intra':
+        # Intraday features
+        data['CurrentOpen30'] = data['Open30'].shift(-1)
+        data['CurrentHigh30'] = data['High30'].shift(-1)
+        data['CurrentLow30'] = data['Low30'].shift(-1)
+        data['CurrentClose30'] = data['Close30'].shift(-1)
+        data['CurrentOHLC430'] = data[['CurrentOpen30','CurrentHigh30','CurrentLow30','CurrentClose30']].max(axis=1)
+        data['OHLC4_Current_Trend'] = data['CurrentOHLC430'] > data['OHLC4']
+        data['OHLC4_Current_Trend'] = data['OHLC4_Current_Trend'].astype(bool)
+        data['HistClose30toPrevClose'] = (data['Close30'] / data['PrevClose']) - 1
+
+        data['CurrentCloseVIX30'] = data['Close_VIX30'].shift(-1)
+        data['CurrentOpenVIX30'] = data['Open_VIX30'].shift(-1)
+
+        data['CurrentVIXTrend'] = data['CurrentCloseVIX30'] > data['Close_VIX']
+
+        # Open to High
+        data['CurrentHigh30toClose'] = (data['CurrentHigh30'] / data['Close']) - 1
+        data['CurrentLow30toClose'] = (data['CurrentLow30'] / data['Close']) - 1
+        data['CurrentClose30toClose'] = (data['CurrentClose30'] / data['Close']) - 1
+        data['CurrentRange30'] = (data['CurrentHigh30'] - data['CurrentLow30']) / data['Close']
+        data['GapFill30'] = [low <= prev_close if gap > 0 else high >= prev_close for high, low, prev_close, gap in zip(data['CurrentHigh30'], data['CurrentLow30'], data['Close'], data['CurrentGap'])]
+        data['CloseL1'] = np.where(data['Close30'] < data['L1'], 1, 0)
+        data['CloseL2'] = np.where(data['Close30'] < data['L2'], 1, 0)
+        data['CloseH1'] = np.where(data['Close30'] > data['H1'], 1, 0)
+        data['CloseH2'] = np.where(data['Close30'] > data['H2'], 1, 0)
+        data['CloseL1'] = data['CloseL1'].shift(-1)
+        data['CloseL2'] = data['CloseL2'].shift(-1)
+        data['CloseH1'] = data['CloseH1'].shift(-1)
+        data['CloseH2'] = data['CloseH2'].shift(-1)
+
+        def get_quintiles(df, col_name, q):
+            return df.groupby(pd.qcut(df[col_name], q))['GreenDay'].mean()
+
+        probas = []
+        # Given the current price level
+        for i, pct in enumerate(data['CurrentClose30toClose']):
+            try:
+                # Split
+                df_q = get_quintiles(data.iloc[:i], 'HistClose30toPrevClose', 10)
+                for q in df_q.index:
+                    if q.left <= pct <= q.right:
+                        p = df_q[q]
+            except:
+                p = None
+
+            probas.append(p)
+
+        data['GreenProbas'] = probas
+
+    engine = create_engine(
+        f"mysql+mysqldb://{os.getenv('DATABASE_USERNAME')}:" \
+        f"{os.getenv('DATABASE_PASSWORD')}@{os.getenv('DATABASE_HOST')}/" \
+        f"{os.getenv('DATABASE')}?ssl_ca=ca-certificates.crt&ssl_mode=VERIFY_IDENTITY"
+    )
+
+    df_releases = pd.read_sql_query('select * from releases', con=engine)
+    df_releases = df_releases.set_index('Datetime')
+    data = data.merge(df_releases, how = 'left', left_index=True, right_index=True)
+
+    for n in tqdm(df_releases.columns, desc='Merging econ data'):
+        # Get the name of the release
+        # n = releases[rid]['name']
+        # Merge the corresponding DF of the release
+        # data = data.merge(releases[rid]['df'], how = 'left', left_index=True, right_index=True)
+        # Create a column that shifts the value in the merged column up by 1
+        data[f'{n}_shift'] = data[n].shift(-1)
+        # Fill the rest with zeroes
+        data[n] = data[n].fillna(0)
+        data[f'{n}_shift'] = data[f'{n}_shift'].fillna(0)
+        
+    data['BigNewsDay'] = data[[x for x in data.columns if '_shift' in x]].max(axis=1)
+
+    def cumul_sum(col):
+        nums = []
+        s = 0
+        for x in col:
+            if x == 1:
+                s += 1
+            elif x == 0:
+                s = 0
+            nums.append(s)
+        return nums
+
+    consec_green = cumul_sum(data['GreenDay'].values)
+    consec_red = cumul_sum(data['RedDay'].values)
+
+    data['DaysGreen'] = consec_green
+    data['DaysRed'] = consec_red
+
+    final_row = data.index[-2]
+
+    if mode=='daily':
+        from dailyCols import model_cols
+
+    elif mode=='intra':
+        from intraCols import model_cols
+
+    df_final = data.loc[:final_row, model_cols + ['Target', 'Target_clf']]
+    df_final = df_final.dropna(subset=['Target','Target_clf'])
+    # df_final = df_final.dropna(subset=['Target','Target_clf','Perf5Day_n1'])
+    return data, df_final, final_row

getIntraData.py

@@ -0,0 +1,140 @@
+import pandas as pd
+import pandas_datareader as pdr
+import yfinance as yf
+import datetime
+# from datasets import load_dataset
+from sqlalchemy import create_engine
+import os
+from getDailyData import data_start_date
+from dotenv import load_dotenv
+
+# Load environment variables from the .env file
+load_dotenv()
+
+def get_intra(periods_30m = 1):
+    '''
+    Method to get historical 30 minute data and append live data to it, if exists. 
+    '''
+    engine = create_engine(
+        f"mysql+mysqldb://{os.getenv('DATABASE_USERNAME')}:" \
+        f"{os.getenv('DATABASE_PASSWORD')}@{os.getenv('DATABASE_HOST')}/" \
+        f"{os.getenv('DATABASE')}?ssl_ca=ca-certificates.crt&ssl_mode=VERIFY_IDENTITY"
+    )
+
+    query = f'''SELECT
+        spx30.Datetime AS Datetime,
+        spx30.Open AS Open30,
+        spx30.High AS High30,
+        spx30.Low AS Low30,
+        spx30.Close AS Close30,
+        vix30.Open AS Open_VIX30,
+        vix30.High AS High_VIX30,
+        vix30.Low AS Low_VIX30,
+        vix30.Close AS Close_VIX30,
+        vvix30.Open AS Open_VVIX30,
+        vvix30.High AS High_VVIX30,
+        vvix30.Low AS Low_VVIX30,
+        vvix30.Close AS Close_VVIX30
+    FROM 
+        SPX_full_30min AS spx30
+    LEFT JOIN 
+        VIX_full_30min AS vix30 ON spx30.Datetime = vix30.Datetime AND vix30.Datetime > {data_start_date}
+    LEFT JOIN 
+        VVIX_full_30min AS vvix30 ON spx30.Datetime = vvix30.Datetime AND vvix30.Datetime > {data_start_date}
+    WHERE 
+        spx30.Datetime > {data_start_date}
+
+    '''
+    # spx30 = pd.read_sql_query(f'SELECT * FROM SPX_full_30min WHERE Datetime > {data_start_date}', con=engine)
+    # vix30 = pd.read_sql_query(f'SELECT * FROM VIX_full_30min WHERE Datetime > {data_start_date}', con=engine)
+    # vvix30 = pd.read_sql_query(f'SELECT * FROM VVIX_full_30min WHERE Datetime > {data_start_date}', con=engine)
+    # dfs = []
+
+    df_30m = pd.read_sql_query(sql=query, con=engine)
+    df_30m['Datetime'] = df_30m['Datetime'].dt.tz_localize('America/New_York')
+    df_30m = df_30m.set_index('Datetime',drop=True)
+
+    # for fr in [spx30, vix30, vvix30]:
+    #     # fr['Datetime'] = fr['Datetime'].apply(lambda x: datetime.datetime.strptime(x[:-6], dt_format))
+    #     fr['Datetime'] = fr['Datetime'].dt.tz_localize('America/New_York')
+    #     fr = fr.set_index('Datetime')
+    #     fr['Open'] = pd.to_numeric(fr['Open'])
+    #     fr['High'] = pd.to_numeric(fr['High'])
+    #     fr['Low'] = pd.to_numeric(fr['Low'])
+    #     fr['Close'] = pd.to_numeric(fr['Close'])
+    #     dfs.append(fr[['Open','High','Low','Close']])
+
+    # df_30m = pd.concat(dfs, axis=1)
+
+    # df_30m.columns = [
+    #     'Open30',
+    #     'High30',
+    #     'Low30',
+    #     'Close30',
+    #     'Open_VIX30',
+    #     'High_VIX30',
+    #     'Low_VIX30',
+    #     'Close_VIX30',
+    #     'Open_VVIX30',
+    #     'High_VVIX30',
+    #     'Low_VVIX30',
+    #     'Close_VVIX30'
+    # ]
+
+    # Get incremental date
+    last_date = df_30m.index.date[-1]
+    last_date = last_date + datetime.timedelta(days=1)
+
+    # Get incremental data for each index
+    spx1 = yf.Ticker('^GSPC')
+    vix1 = yf.Ticker('^VIX')
+    vvix1 = yf.Ticker('^VVIX')    
+    yfp = spx1.history(start=last_date, interval='30m')
+    yf_vix = vix1.history(start=last_date, interval='30m')
+    yf_vvix = vvix1.history(start=last_date, interval='30m')
+
+    if len(yfp) > 0:
+        # Convert indexes to EST if not already
+        for _df in [yfp, yf_vix, yf_vvix]:
+            if (_df.index.tz.zone != 'America/New_York') or (type(_df.index) != pd.DatetimeIndex):
+                _df['Datetime'] = pd.to_datetime(_df.index)
+                _df['Datetime'] = _df['Datetime'].dt.tz_convert('America/New_York')
+                _df.set_index('Datetime', inplace=True)
+        # Concat them 
+        df_inc = pd.concat([
+            yfp[['Open','High','Low','Close']], 
+            yf_vix[['Open','High','Low','Close']], 
+            yf_vvix[['Open','High','Low','Close']]
+            ], axis=1)
+        df_inc.columns = df_30m.columns
+        df_inc = df_inc.loc[
+            (df_inc.index.time >= datetime.time(9,30)) & (df_inc.index.time < datetime.time(16,00))
+        ]
+        df_30m = pd.concat([df_30m, df_inc])
+    else:
+        df_30m = df_30m.copy()
+
+    df_30m = df_30m.loc[
+                (df_30m.index.time >= datetime.time(9,30)) & (df_30m.index.time < datetime.time(16,00))
+            ]
+    df_30m['dt'] = df_30m.index.date
+    df_30m = df_30m.groupby('dt').head(periods_30m)
+    df_30m = df_30m.set_index('dt',drop=True)
+    df_30m.index.name = 'Datetime'
+
+    df_30m['SPX30IntraPerf'] = (df_30m['Close30'] / df_30m['Close30'].shift(1)) - 1
+    df_30m['VIX30IntraPerf'] = (df_30m['Close_VIX30'] / df_30m['Close_VIX30'].shift(1)) - 1
+    df_30m['VVIX30IntraPerf'] = (df_30m['Close_VVIX30'] / df_30m['Close_VVIX30'].shift(1)) - 1
+
+    opens_intra = df_30m.groupby('Datetime')[[c for c in df_30m.columns if 'Open' in c]].head(1)
+    highs_intra = df_30m.groupby('Datetime')[[c for c in df_30m.columns if 'High' in c]].max()
+    lows_intra = df_30m.groupby('Datetime')[[c for c in df_30m.columns if 'Low' in c]].min()
+    closes_intra = df_30m.groupby('Datetime')[[c for c in df_30m.columns if 'Close' in c]].tail(1)
+    spx_intra = df_30m.groupby('Datetime')['SPX30IntraPerf'].tail(1)
+    vix_intra = df_30m.groupby('Datetime')['VIX30IntraPerf'].tail(1)
+    vvix_intra = df_30m.groupby('Datetime')['VVIX30IntraPerf'].tail(1)
+
+    df_intra = pd.concat([opens_intra, highs_intra, lows_intra, closes_intra, spx_intra, vix_intra, vvix_intra], axis=1)
+    return df_intra
+
+    

intraCols.py

@@ -0,0 +1,48 @@
+model_cols = [
+    'BigNewsDay',
+    'Quarter',
+    'Perf5Day',
+    'Perf5Day_n1',
+    'DaysGreen',
+    'DaysRed',
+    'CurrentHigh30toClose',
+    'CurrentLow30toClose',
+    'CurrentClose30toClose',
+    'CurrentRange30',
+    'GapFill30',
+    'CurrentGap',
+    'RangePct',
+    'RangePct_n1',
+    'RangePct_n2',
+    'OHLC4_VIX',
+    'OHLC4_VIX_n1',
+    'OHLC4_VIX_n2',
+    'OHLC4_Current_Trend',
+    'OHLC4_Trend',
+    'CurrentVIXTrend',
+    'SPX30IntraPerf',
+    'VIX30IntraPerf',
+    'VVIX30IntraPerf',
+    # 'OpenL1',
+    # 'OpenL2',
+    # 'OpenH1',
+    # 'OpenH2',
+    'L1TouchPct',
+    'L2TouchPct',
+    'H1TouchPct',
+    'H2TouchPct',
+    'L1BreakPct',
+    'L2BreakPct',
+    'H1BreakPct',
+    'H2BreakPct',
+    'GreenProbas',
+    'H1BreakTouchPct',
+    'H2BreakTouchPct',
+    'L1BreakTouchPct',
+    'L2BreakTouchPct',
+    'H1BreakH2TouchPct',
+    'L1BreakL2TouchPct',
+    'H1TouchGreenPct',    
+    'L1TouchRedPct'    
+    # 'GapFillGreenProba'
+]

model_day_v2.py

@@ -0,0 +1,118 @@
+import pandas as pd
+import pandas_datareader as pdr
+import yfinance as yf
+import requests
+from bs4 import BeautifulSoup
+from typing import List
+from tqdm import tqdm
+from sklearn import linear_model
+import os
+import lightgbm as lgb
+from dailyCols import model_cols
+
+def walk_forward_validation(df, target_column, num_training_rows, num_periods):
+    
+    # Create an XGBRegressor model
+    # model = xgb.XGBRegressor(n_estimators=100, objective='reg:squarederror', random_state = 42)
+    model = linear_model.LinearRegression()
+
+    overall_results = []
+    # Iterate over the rows in the DataFrame, one step at a time
+    for i in tqdm(range(num_training_rows, df.shape[0] - num_periods + 1),desc='LR Model'):
+        # Split the data into training and test sets
+        X_train = df.drop(target_column, axis=1).iloc[:i]
+        y_train = df[target_column].iloc[:i]
+        X_test = df.drop(target_column, axis=1).iloc[i:i+num_periods]
+        y_test = df[target_column].iloc[i:i+num_periods]
+        
+        # Fit the model to the training data
+        model.fit(X_train, y_train)
+        
+        # Make a prediction on the test data
+        predictions = model.predict(X_test)
+        
+        # Create a DataFrame to store the true and predicted values
+        result_df = pd.DataFrame({'True': y_test, 'Predicted': predictions}, index=y_test.index)
+        
+        overall_results.append(result_df)
+
+    df_results = pd.concat(overall_results)
+    # model.save_model('model_lr.bin')
+    # Return the true and predicted values, and fitted model
+    return df_results, model
+
+def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_training_rows, num_periods):
+
+    # Create run the regression model to get its target
+    res, model1 = walk_forward_validation(df.drop(columns=[target_column_clf]).dropna(), target_column_regr, num_training_rows, num_periods)
+    # joblib.dump(model1, 'model1.bin')
+
+    # Merge the result df back on the df for feeding into the classifier
+    for_merge = res[['Predicted']]
+    for_merge.columns = ['RegrModelOut']
+    for_merge['RegrModelOut'] = for_merge['RegrModelOut'] > 0
+    df = df.merge(for_merge, left_index=True, right_index=True)
+    df = df.drop(columns=[target_column_regr])
+    df = df[model_cols + ['RegrModelOut', target_column_clf]]
+    
+    df[target_column_clf] = df[target_column_clf].astype(bool)
+    df['RegrModelOut'] = df['RegrModelOut'].astype(bool)
+
+    # Create an XGBRegressor model
+    # model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    model2 = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1)
+    # model = linear_model.LogisticRegression(max_iter=1500)
+    
+    overall_results = []
+    # Iterate over the rows in the DataFrame, one step at a time
+    for i in tqdm(range(num_training_rows, df.shape[0] - num_periods + 1),'CLF Model'):
+        # Split the data into training and test sets
+        X_train = df.drop(target_column_clf, axis=1).iloc[:i]
+        y_train = df[target_column_clf].iloc[:i]
+        X_test = df.drop(target_column_clf, axis=1).iloc[i:i+num_periods]
+        y_test = df[target_column_clf].iloc[i:i+num_periods]
+        
+        # Fit the model to the training data
+        model2.fit(X_train, y_train)
+        
+        # Make a prediction on the test data
+        predictions = model2.predict_proba(X_test)[:,-1]
+        
+        # Create a DataFrame to store the true and predicted values
+        result_df = pd.DataFrame({'True': y_test, 'Predicted': predictions}, index=y_test.index)
+        
+        overall_results.append(result_df)
+
+    df_results = pd.concat(overall_results)
+
+    # Calibrate Probabilities
+    def get_quantiles(df, col_name, q):
+        return df.groupby(pd.cut(df[col_name], q))['True'].mean()
+
+    greenprobas = []
+    meanprobas = []
+    for i, pct in tqdm(enumerate(df_results['Predicted']), desc='Calibrating Probas'):
+        try:
+            df_q = get_quantiles(df_results.iloc[:i], 'Predicted', 7)
+            for q in df_q.index:
+                if q.left <= pct <= q.right:
+                    p = df_q[q]
+                    c = (q.left + q.right) / 2
+        except:
+            p = None
+            c = None
+
+        greenprobas.append(p)
+        meanprobas.append(c)
+
+    df_results['CalibPredicted'] = greenprobas
+    
+    return df_results, model1, model2
+
+def seq_predict_proba(df, trained_reg_model, trained_clf_model):
+    regr_pred = trained_reg_model.predict(df)
+    regr_pred = regr_pred > 0
+    new_df = df.copy()
+    new_df['RegrModelOut'] = regr_pred
+    clf_pred_proba = trained_clf_model.predict_proba(new_df[model_cols + ['RegrModelOut']])[:,-1]
+    return clf_pred_proba

model_intra_v2.py

@@ -0,0 +1,73 @@
+import streamlit as st
+import pandas as pd
+import pandas_datareader as pdr
+import numpy as np
+import yfinance as yf
+import requests
+from bs4 import BeautifulSoup
+from typing import List
+from tqdm import tqdm
+import os
+import datetime
+from pandas.tseries.offsets import BDay
+from datasets import load_dataset
+import lightgbm as lgb
+from sklearn.model_selection import TimeSeriesSplit
+from intraCols import model_cols
+
+# If the dataset is gated/private, make sure you have run huggingface-cli login
+def walk_forward_validation(df, target_column, num_periods):
+    
+    df = df[model_cols + [target_column]]
+    df[target_column] = df[target_column].astype(bool)
+
+    # Model
+    # model = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1)
+
+    tscv = TimeSeriesSplit(n_splits=len(df)-1, max_train_size=None, test_size=num_periods)  # num_splits is the number of splits you want
+
+    overall_results = []
+    # Iterate over the rows in the DataFrame, one step at a time
+    # Split the time series data using TimeSeriesSplit
+    for train_index, test_index in tqdm(tscv.split(df), total=tscv.n_splits):
+        # Extract the training and testing data for the current split
+        X_train = df.drop(target_column, axis=1).iloc[train_index]
+        y_train = df[target_column].iloc[train_index]
+        X_test = df.drop(target_column, axis=1).iloc[test_index]
+        y_test = df[target_column].iloc[test_index]
+    
+        y_train = y_train.astype(bool)
+        model = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1)
+        model.fit(X_train, y_train)
+        # Make a prediction on the test data
+        predictions = model.predict_proba(X_test)[:,-1]
+            
+        # Create a DataFrame to store the true and predicted values
+        result_df = pd.DataFrame({'True': y_test, 'Predicted': predictions}, index=y_test.index)
+        overall_results.append(result_df)
+
+    df_results = pd.concat(overall_results)
+
+    # Calibrate Probabilities
+    def get_quantiles(df, col_name, q):
+        return df.groupby(pd.cut(df[col_name], q))['True'].mean()
+
+    greenprobas = []
+    for i, pct in tqdm(enumerate(df_results['Predicted']), desc='Calibrating Probas',total=len(df_results)):
+        try:
+            df_q = get_quantiles(df_results.iloc[:i], 'Predicted', 7)
+            for q in df_q.index:
+                if q.left <= pct <= q.right:
+                    p = df_q[q]
+        except:
+            p = None
+
+        greenprobas.append(p)
+
+    df_results['CalibPredicted'] = greenprobas
+
+    return df_results, model
+
+def seq_predict_proba(df, trained_clf_model):
+    clf_pred_proba = trained_clf_model.predict_proba(df[model_cols])[:,-1]
+    return clf_pred_proba

requirements.txt

@@ -17,4 +17,5 @@ scipy
 datasets
 huggingface_hub
 holidays
-pytz
+pytz
+sqlalchemy