#region imports
from AlgorithmImports import *
#endregion
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import yfinance as yf
import pandas_ta as ta
from IPython.core.display import display, HTML
from collections import Counter
import time
from datetime import datetime

from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,VotingClassifier,StackingClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC as SupportVectorClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split,GridSearchCV,cross_val_score,KFold,StratifiedKFold 
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,confusion_matrix,roc_curve, roc_auc_score

def symbolShift(df, num_shift, drop_first_shift=True, Label=True , shifted_column='Close'): 
    X = df[shifted_column]
    shifted_dict = {}
    shifted_dict[shifted_column] = df[shifted_column]
    
    for i in range(1,num_shift+1):
        shifted_series = X.shift(i)
        shifted_dict[f'sh{i}'] = shifted_series
    
    X2 = pd.DataFrame(shifted_dict)
    
    if drop_first_shift == True :
        X2 = X2[num_shift::]
    
    if Label == True :
        X2['Label'] = X2['returns']-X2['sh1']
        X2.loc[X2['Label'] < 0 , 'Label'] = 0
        X2.loc[X2['Label'] > 0 , 'Label'] = 1
    
    return X2
 
def InputOutput(dataframe, label, traintest=True, split_size=None):
    X = dataframe.drop(label, axis=1)
    y = dataframe[label]
    
    if traintest == True:
        Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size = split_size)
        return Xtrain, Xtest, ytrain, ytest
    
    else:
        return X,y
    return fig.show()

def slicingTimeseries(X,y,sliceTrain=0.8):
    slicTrain = X.shape[0]*sliceTrain

    Xtrain = X[0:int(slicTrain)]
    Xtest = X[int(slicTrain):]
    ytrain = y[0:int(slicTrain)]
    ytest = y[int(slicTrain):]
    
    return Xtrain , Xtest , ytrain , ytest

     
def AddOsil(df):
    df['returns'] = df['Close'].pct_change()
    df['RSI']=ta.rsi(df.Close, length=15)
    df['EMAF']=ta.ema(df.Close, length=20)
    df['EMAM']=ta.ema(df.Close, length=100)
    df['EMAS']=ta.ema(df.Close, length=150)
    
    return df 

def AddLabel(df):
    df['Label'] = df['Close']-df['Open']
    df.loc[df['Label'] < 0 , 'Label'] = 0
    df.loc[df['Label'] > 0 , 'Label'] = 1
    df['Label2'] = df['Label'].shift(-1)
    return df
   
def TimeSeriesGen(df , shift):
    y = df['Label2']
    X = df.drop(['Label','Label2','Open','High','Low'],axis=1)
    Z = X.copy()
    for i in range(shift):
        i = i+1
        
        X = pd.concat([X, Z.shift(i)], axis=1)
        
    return X,y

def backtester(data , model , amount=10000 , fee=0.01 , period=400 , lev=1):
    buy = 0
    sell = 0
    baseAmount = amount
    borrow = amount*(lev-1)
    amount = amount*lev
                
    for i in range(1,period):
        ytomo = model.predict(data.iloc[i-1:i])
        if ytomo == 1 :
            buy +=1
            amount -=fee
            amount=amount*((data.iloc[i:i+1].returns.iloc[0][0] * 100) +100)/ 100   
            
        elif ytomo == 0 :
            sell +=1
            amount -=fee
            amount=amount*(-(data.iloc[i:i+1].returns.iloc[0][0] * 100) +100)/ 100
                
        #print(amount) 
        if amount <= borrow:

            break
        
    netprof = amount-borrow

    return netprof

def Buybacktester(data , amount=10000 , fee=0.01 , period=400 , lev=1):
    buy = 0
    sell = 0
    baseAmount = amount
    borrow = amount*(lev-1)
    amount = amount*lev
    
    for i in range(1,period):
        ytomo =1
 
        if ytomo == 1 :
            buy +=1
            amount -=fee
            amount=amount*((data.iloc[i-1:i].returns.iloc[0][0] * 100) +100)/ 100
 
            
        elif ytomo == 0 :
            sell +=1
            amount -=fee
            amount=amount*(-(data.iloc[i-1:i].returns.iloc[0][0] * 100) +100)/ 100

                
        #print(amount) 
        if amount <= borrow:
            print('Call Marjin')
            break

    netprof = amount-borrow
    return netprof

def BaselineModels(Xlearn,ylearn,Xval,yval):
    
    Model = {}  
    
    Model['GBC']  = {'model': GradientBoostingClassifier()}
    Model['LRC']  = {'model': LogisticRegression()}
    Model['DTC']  = {'model': DecisionTreeClassifier()} 
    Model['RFC']  = {'model':RandomForestClassifier()}
    Model['KNC']  = {'model': KNeighborsClassifier()} #7
    Model['GNB']  = {'model': GaussianNB()}
    Model['LSVC'] = {'model': LinearSVC()}
    Model['SVC']  = {'model': SupportVectorClassifier()}
    Model['MLP']  = {'model': MLPClassifier()}    
    Model['VOT'] = {'model': VotingClassifier(estimators=[('GBC', Model['GBC']['model']), ('LRC', Model['LRC']['model']), ('GNB', Model['GNB']['model']),
                                            ('DTC', Model['DTC']['model']), ('KNC', Model['KNC']['model']), ('RFC', Model['RFC']['model']),
                                            ('LSVC', Model['LSVC']['model']), ('SVC', Model['SVC']['model']), ('MLP', Model['MLP']['model'])
                                           ],
                            voting='hard')}     
    Model['STK'] = {'model': StackingClassifier(estimators=[('GBC', Model['GBC']['model']), ('LRC', Model['LRC']['model']), ('GNB', Model['GNB']['model']),
                                                ('DTC', Model['DTC']['model']), ('KNC', Model['KNC']['model']), ('RFC', Model['RFC']['model']),
                                                ('LSVC', Model['LSVC']['model']), ('SVC', Model['SVC']['model']), ('MLP', Model['MLP']['model'])
                                               ],
                            final_estimator=LogisticRegression())}
    
    #----------------------------------------------------
    counts = Counter(yval)
    print('norm yval count:',counts)   
    NormalizeAcc = counts[1] / (counts[0]+counts[1])
    print(NormalizeAcc)   
    if(NormalizeAcc < 0.5): 
        NormalizeAcc = 1 - NormalizeAcc
    print(NormalizeAcc)    
    #----------------------------------------------------
        
    for i in Model.keys():
        Model[i]['model'].fit(Xlearn, ylearn)
        ypred = Model[i]['model'].predict(Xval)
        accuracy = accuracy_score(yval, ypred)
        precision = precision_score(yval, ypred)
        recall = recall_score(yval, ypred)
        f1 = f1_score(yval, ypred)
    
        NormalizedAcc = (accuracy * 0.5) /  NormalizeAcc    
        backtest = backtester(Xval ,model=Model[i]['model'] ,period = Xval.shape[0], lev=1) 
        nnp = Buybacktester(Xval , amount=10000 , fee=0.01 , period=Xval.shape[0] , lev=1)
        Model[i]['acc'] = accuracy
        Model[i]['backtest'] = backtest
        Model[i]['nnp'] = nnp
        Model[i]['NormalizedAcc'] = NormalizedAcc
        Model[i]['precision'] = precision
        Model[i]['recall'] = recall
        Model[i]['f1'] = f1
        

    return Model
#--------------------------------------------------------------------------------------------------------
def ParamsModels(Xlearn,ylearn,Xval,yval):
    
    paramModel = {}  

    paramModel['GBC']  = {'model': GradientBoostingClassifier(n_estimators=100, learning_rate=0.01, max_depth=12)}
    paramModel['LRC']  = {'model': LogisticRegression(C=0.1, max_iter=10000, solver='lbfgs')}
    paramModel['DTC']  = {'model': DecisionTreeClassifier(max_depth = 12, min_samples_split=6, min_samples_leaf=4)} 
    paramModel['RFC']  = {'model': RandomForestClassifier(n_estimators = 500, max_depth = 10)}
    paramModel['KNC']  = {'model': KNeighborsClassifier(n_neighbors = 7, weights='distance')} 
    paramModel['GNB']  = {'model': GaussianNB(var_smoothing=1e-09)}
    paramModel['LSVC'] = {'model': LinearSVC(C=1 ,max_iter = 10000)}
    paramModel['SVC']  = {'model': SupportVectorClassifier(C=1, kernel='rbf', gamma='scale', max_iter=5000)}
    paramModel['MLP'] = {'model': MLPClassifier(hidden_layer_sizes = (69),max_iter = 10000 ,activation='relu', alpha=0.0001)}     
    paramModel['VOT'] = {'model': VotingClassifier(estimators=[('GBC', paramModel['GBC']['model']), ('LRC', paramModel['LRC']['model']), ('GNB', paramModel['GNB']['model']),
                                            ('DTC', paramModel['DTC']['model']), ('KNC', paramModel['KNC']['model']), ('RFC', paramModel['RFC']['model']),
                                            ('LSVC', paramModel['LSVC']['model']), ('SVC', paramModel['SVC']['model']), ('MLP', paramModel['MLP']['model'])
                                           ],
                                  voting='hard')}     
    paramModel['STK'] = {'model': StackingClassifier(estimators=[('GBC', paramModel['GBC']['model']), ('LRC', paramModel['LRC']['model']), ('GNB', paramModel['GNB']['model']),
                                                ('DTC', paramModel['DTC']['model']), ('KNC', paramModel['KNC']['model']), ('RFC', paramModel['RFC']['model']),
                                                ('LSVC', paramModel['LSVC']['model']), ('SVC', paramModel['SVC']['model']), ('MLP', paramModel['MLP']['model'])
                                               ],
                                  final_estimator=LogisticRegression())}
    
    #----------------------------------------------------
    counts = Counter(yval)

    NormalizeAcc = counts[1] / (counts[0]+counts[1])

    if(NormalizeAcc < 0.5): 
        NormalizeAcc = 1 - NormalizeAcc
    
    #----------------------------------------------------
    
    for i in paramModel.keys():
        paramModel[i]['model'].fit(Xlearn, ylearn)
        ypred = paramModel[i]['model'].predict(Xval)
        accuracy = accuracy_score(yval, ypred)
        precision = precision_score(yval, ypred)
        recall = recall_score(yval, ypred)
        f1 = f1_score(yval, ypred)
        
        NormalizedAcc = (accuracy * 0.5) /  NormalizeAcc  
        backtest = backtester(Xval ,paramModel[i]['model'] ,period=Xval.shape[0] ,lev=1) 
        nnp = Buybacktester(Xval , amount=10000 , fee=0.01 , period=Xval.shape[0] , lev=1)
        paramModel[i]['acc'] = accuracy
        paramModel[i]['backtest'] = backtest
        paramModel[i]['nnp'] = nnp
        paramModel[i]['NormalizedAcc'] = NormalizedAcc
        paramModel[i]['precision'] = precision
        paramModel[i]['recall'] = recall
        paramModel[i]['f1'] = f1
       
        
    return paramModel   
#-------------------------------------------------------------------------------------------------------
def GridSearchModels(Xlearn,ylearn,Xval,yval):

    param_grids = {
        'GBC': {'n_estimators': [50, 100, 200], 'learning_rate': [0.01, 0.1, 1], 'max_depth': [5, 7, 10, 12]},
        'LRC': {'C': [0.1, 1, 10]},
        'DTC': {'max_depth': [3, 5, 7], 'min_samples_split': [4,5,6,7], 'min_samples_leaf': [3,4,5,6]},
        'RFC': {'n_estimators': [50, 100, 200, 400, 600], 'max_depth': [3, 5, 7 ,10 ,14]},
        'KNC': {'n_neighbors': [3, 5, 7, 10, 14]},
        'GNB': {},  # No hyperparameters for GaussianNB
        'LSVC': {'C': [0.1, 1, 10] ,'max_iter' : [1000,5000,10000,20000,40000]},
        'SVC': {'max_iter': [1000,5000,10000,20000,40000],'C': [0.1, 1, 10], 'gamma': ['auto', 'scale']},
        'MLP': {'hidden_layer_sizes': [(20,), (50,), (100,)], 'max_iter': [10000 , 20000]}
    }

    models = {
        'GBC': GradientBoostingClassifier(),
        'LRC': LogisticRegression(),
        'DTC': DecisionTreeClassifier(),
        'RFC': RandomForestClassifier(),
        'KNC': KNeighborsClassifier(),
        'GNB': GaussianNB(),
        'LSVC': LinearSVC(),
        'SVC': SupportVectorClassifier(),
        'MLP': MLPClassifier()
    }
    
    #----------------------------------------------------
    counts = Counter(yval)
    NormalizeAcc = counts[1] / (counts[0]+counts[1])
    if(NormalizeAcc < 0.5): 
        NormalizeAcc = 1 - NormalizeAcc
    #----------------------------------------------------

    trained_models = {}
    for name, model in models.items():

        grid_search = GridSearchCV(model, param_grids[name], cv=5, scoring='accuracy', n_jobs=-1)
        grid_search.fit(Xlearn, ylearn)

        best_model = grid_search.best_estimator_
        #print(f"Best hyperparameters for {name}: {grid_search.best_params_}")

        best_model.fit(Xlearn, ylearn)
        ypred = best_model.predict(Xval)
        accuracy = accuracy_score(yval, ypred)
        precision = precision_score(yval, ypred)
        recall = recall_score(yval, ypred)
        f1 = f1_score(yval, ypred)
        auc = roc_auc_score(yval, ypred)
        NormalizedAcc = (accuracy * 0.5) /  NormalizeAcc  
        backtest = backtester(Xval ,best_model ,period=Xval.shape[0] ,lev=1) 
        nnp = Buybacktester(Xval , amount=10000 , fee=0.01 , period=Xval.shape[0] , lev=1)
        
        trained_models[name] = {'model': best_model, 
                                'acc': accuracy , 
                                'backtest' : backtest,
                                'nnp' : nnp,
                                'NormalizedAcc' : NormalizedAcc,
                                'precision' : precision,
                                'recall' : recall,
                                'f1' : f1,
                                'auc' : auc}
        
        
        #print(f"Best hyperparameters: {grid_search.best_params_}")
        #print(f"GS Test accuracy: {accuracy} Norm Test accuracy:{Naccuracy}  \n")   
        
    return trained_models
   
#--------------------------------------------------------------------------------------------------------
def Main(data, gridsearch=False , shift=10):

    data = AddOsil(data)
    data = AddLabel(data)
    X,y = TimeSeriesGen(data , shift)
    Xtrain , Xtest , ytrain , ytest = slicingTimeseries(X[160:-1:],y[160:-1:],sliceTrain=0.90)#95
    Xlearn, Xval, ylearn, yval = slicingTimeseries(Xtrain,ytrain,sliceTrain=0.90)#7
    
    dic1  = BaselineModels(Xlearn,ylearn,Xval,yval)
    
    if gridsearch == False:
        dic2  = ParamsModels(Xlearn,ylearn,Xval,yval)
        print('Models-------NorNormAcc:---------ParNormAcc--------NormalACC:---------ParamACC:')
        for i ,j  in dic1.items():
            for z ,d in dic2.items():
                if i == z :
                     print(i,'===',j['NormalizedAcc'],d['NormalizedAcc'],j['acc'],d['acc'])

        return dic1,dic2,Xtest,ytest
    
    elif gridsearch == True:
        dicGS = GridSearchModels(Xlearn,ylearn,Xval,yval)   
        print('Models-------NorNormAcc:---------GridNormAcc--------NormalACC:---------GridACC:')
        for i ,j  in dic1.items():
            for z ,d in dicGS.items():
                if i == z :
                    print(i,'===',j['NormalizedAcc'],d['NormalizedAcc'],j['acc'],d['acc'])

        return dic1,dicGS,Xtest,ytest
#--------------------------------------------------------------------------------------------------------
def CreateDF(Maindf):

    index = []
    data = {}
    data['model'] = []
    data['Valacc'] = []
    data['ValNormalizedAcc'] = []
    data['Valprecision'] = []
    data['Valrecall'] = []
    data['Valf1'] = []
   
    data['Valbacktest'] = []
    data['Valnnp'] = []

    for i in Maindf.keys():
        for j,k in Maindf[i]['BModel'].items():
            index.append(i+' N')
            data['model'].append(k['model'])
            data['Valacc'].append(k['acc'])
            data['ValNormalizedAcc'].append(k['NormalizedAcc'])
            data['Valprecision'].append(k['precision'])
            data['Valrecall'].append(k['recall'])
            data['Valf1'].append(k['f1'])
            
            data['Valbacktest'].append(k['backtest'])
            data['Valnnp'].append(k['nnp'])
        for n,m in Maindf[i]['PModel'].items():
            index.append(i+' P')
            data['model'].append(m['model'])
            data['Valacc'].append(m['acc'])
            data['ValNormalizedAcc'].append(m['NormalizedAcc'])
            data['Valprecision'].append(m['precision'])
            data['Valrecall'].append(m['recall'])
            data['Valf1'].append(m['f1'])
            
            data['Valbacktest'].append(m['backtest'])
            data['Valnnp'].append(m['nnp'])
            
        if 'GModel' in Maindf[i].keys() :
            for e,r in Maindf[i]['GModel'].items():
                index.append(i+' G')
                data['model'].append(r['model'])
                data['Valacc'].append(r['acc'])
                data['ValNormalizedAcc'].append(r['NormalizedAcc'])
                data['Valprecision'].append(r['precision'])
                data['Valrecall'].append(r['recall'])
                data['Valf1'].append(r['f1'])
                
                data['Valbacktest'].append(r['backtest'])
                data['Valnnp'].append(r['nnp'])
        else:
            print("GModel does not exist in row", i)    


    df = pd.DataFrame(data, index=index)
    df ['ValProfit %'] = df['Valbacktest']/df['Valnnp']*100

    return df
#-----------------------------------------------------------------------------------------------------
def ADDTestFeatures(df , Maindf):
    c=0
    TESTbackt = []
    nnpt = []

    # TrainAccuracy = []
    #add new featurs to set
    #

    for i in df.index:
        ticker = i[:-2]
        Xt = Maindf[ticker]['Xtest']
        Yt = Maindf[ticker]['ytest']

        #Yp = df['model'].values[c].predict(Xt)
        #accuracy = accuracy_score(Yt , Yp)
        #precision = precision_score(Yt , Yp)
        #recall = recall_score(Yt , Yp)
        #f1 = f1_score(Yt , Yp)
        #auc = roc_auc_score(Yt , Yp)

        backtest = backtester(Xt ,df['model'].values[c] ,period=Xt.shape[0] ,lev=1) 
        Testnnp = Buybacktester(Xt , amount=10000 , fee=0.01 , period=Xt.shape[0] , lev=1)

        c = c+1

        TESTbackt.append(backtest)
        nnpt.append(Testnnp)

    df['Testbacktest'] = TESTbackt
    df['Testnnp'] = nnpt
    
    df ['TestProfit %'] = df['Testbacktest']/df['Testnnp']*100
    df['Label'] = np.where(df['TestProfit %'] > 103, 1, 0)
    return df
#-----------------------------------------------------------------------------------------------------
def TrainValTestSplit(df,test_size = 0.30,val_size = 0.30): 
    y = df['Label']
    X = df
    
    BXtrain, BXtest, Bytrain, Bytest = train_test_split(X, y, test_size = test_size, shuffle=True)
    #, 'Valacc' , 'Valbacktest' , 'Valnnp'
    z = BXtest[['Testbacktest','TestProfit %','model','Testnnp','Label']]
    BXtrain = BXtrain.drop(['Testbacktest','TestProfit %','model','Testnnp','Label'], axis=1)
    BXtest = BXtest.drop(['Testbacktest','TestProfit %','model','Testnnp','Label'], axis=1)
    
    BXlearn, BXval, Bylearn, Byval = train_test_split(BXtrain, Bytrain, test_size = val_size)

    #print(BXtrain.shape, BXtest.shape, Bytrain.shape, Bytest.shape)
    #print(BXlearn.shape, BXval.shape, Bylearn.shape, Byval.shape)
    #print(z.shape)
    return BXlearn, BXval, Bylearn, Byval, BXtest, Bytest, z

#-----------------------------------------------------------------------------------------------------
def BestModelFinder(BXtrain, BXtest, Bytrain, Bytest):
    
    paramModel = {}  

    paramModel['GBC']  = {'model': GradientBoostingClassifier(n_estimators=100, learning_rate=0.01, max_depth=12)}
    paramModel['LRC']  = {'model': LogisticRegression(C=0.1, max_iter=10000, solver='lbfgs')}
    paramModel['DTC']  = {'model': DecisionTreeClassifier(max_depth = 12, min_samples_split=6, min_samples_leaf=4)} 
    paramModel['RFC']  = {'model': RandomForestClassifier(n_estimators = 500, max_depth = 10)}
    paramModel['KNC']  = {'model': KNeighborsClassifier(n_neighbors = 7, weights='distance')} #7
    paramModel['GNB']  = {'model': GaussianNB(var_smoothing=1e-09)}
    paramModel['MLP'] = {'model': MLPClassifier(hidden_layer_sizes = (69),max_iter = 10000 ,activation='relu', alpha=0.0001)}     
    paramModel['VOT'] = {'model': VotingClassifier(estimators=[('GBC', paramModel['GBC']['model']), ('LRC', paramModel['LRC']['model']), 
                                                ('GNB', paramModel['GNB']['model']),('DTC', paramModel['DTC']['model']), ('KNC', paramModel['KNC']['model']), ('RFC', paramModel['RFC']['model']),
                                                ('MLP', paramModel['MLP']['model'])
                                               ],
                                  voting='hard')}     
    paramModel['STK'] = {'model': StackingClassifier(estimators=[('GBC', paramModel['GBC']['model']), ('LRC', paramModel['LRC']['model']), 
                                                ('GNB', paramModel['GNB']['model']),('DTC', paramModel['DTC']['model']), ('KNC', paramModel['KNC']['model']), ('RFC', paramModel['RFC']['model']),
                                                ('MLP', paramModel['MLP']['model'])
                                               ],
                                  final_estimator=LogisticRegression())}
    
    for i in paramModel.keys():
        paramModel[i]['model'].fit(BXtrain, Bytrain)
        
        ypred = paramModel[i]['model'].predict(BXtest)
        accuracy = accuracy_score(Bytest, ypred)
        precision = precision_score(Bytest, ypred)
        recall = recall_score(Bytest, ypred)
        f1 = f1_score(Bytest, ypred)
       
        
        ypred2 = paramModel[i]['model'].predict(BXtrain)
        accuracy2 = accuracy_score(Bytrain, ypred2)
        precision2 = precision_score(Bytrain, ypred2)
        recall2 = recall_score(Bytrain, ypred2)
        f12 = f1_score(Bytrain, ypred2)
        
        
        paramModel[i]['LearnACC'] = accuracy2
        paramModel[i]['Learnprecision'] = precision2
        paramModel[i]['Learnrecall'] = recall2
        paramModel[i]['Learnf1'] = f12
               
        
        paramModel[i]['ValACC'] = accuracy
        paramModel[i]['Valprecision'] = precision
        paramModel[i]['Valrecall'] = recall
        paramModel[i]['Valf1'] = f1
       
        
    return paramModel   
#-----------------------------------------------------------------------------------------------------
def plotFinalModel(model, p, data , title ,lev=1):
    X = data.iloc[-p::,:77:]

    X["output"] = model.predict(X.iloc[::,::])
    X["NormalReturns"] = X["returns"].iloc[:,0].cumsum()

    X["output"] = X["output"].replace(0 , -1) 
    X['outputshift'] = X['output'].shift(1)

    X['strategy'] = X["returns"].iloc[:,0] * X["outputshift"] * lev
    X["StrategyReturns"] = X["strategy"].cumsum()
    
    X[["NormalReturns","StrategyReturns"]].plot(figsize = (12 , 8))
    plt.title(title)
    plt.show() 
#-----------------------------------------------------------------------------------------------------
def plotBestModel(BestDic, BXtest, Bestmetric='ValACC', lev=1, showResultDf=True, label='PLabel', showbest=False):
    
    best_model_name = max(BestDic, key=lambda key: BestDic[key][Bestmetric])
    best_model_test_acc = BestDic[best_model_name]['ValACC']

    if 'PLabel' in BXtest.columns:
        BXtest = BXtest.drop(['PLabel'] , axis=1)

    ypred = BestDic[best_model_name]['model'].predict(BXtest)
    print('best instrument-model selector is:',best_model_name,'with acc:',best_model_test_acc)
    result_df = pd.concat([BXtest, z], axis=1)
    result_df['PLabel'] = ypred
    
    if showbest==True:
        df = result_df[result_df[label] == 1]
        max_row = df[df['Valacc'] == df['Valacc'].max()]
        ticker = max_row.index[0][:-2]
        TestData = Maindf[ticker]['XTest']
        ChosseModel = max_row['model'].values[0]
        display(ChosseModel)
        plotFinalModel(ChosseModel, TestData.shape[0], TestData , ticker , lev=lev)
    else:    
        for i in range(1,result_df[result_df[label] == 1].shape[0]+1):
            ticker = result_df[result_df[label] == 1][i-1:i].index[0][:-2]
            TestData = Maindf[ticker]['XTest']
            ChosseModel = result_df[result_df[label] == 1][i-1:i]['model'].values[0]
            display(ChosseModel)
            plotFinalModel(ChosseModel, TestData.shape[0], TestData , ticker , lev=lev)

        if showResultDf==True:    
             pd.set_option('display.max_columns', 9)
             display(result_df[result_df[label] == 1]) 
             pd.reset_option('display.max_columns') 
#-------------------------------------------------------------------------------------------------    
def PlotSumModel(Finaldf, symbolsreturn):
    DF = pd.DataFrame()  

    DF['ModelsReturn'] = Finaldf["column_sums"]
    DF['NormalReturn'] = symbolsreturn["column_sums"]

    DF[["ModelsReturn","NormalReturn"]].plot(figsize = (12 , 8))

    plt.title('sum of all tradings')
    plt.show() 
#-------------------------------------------------------------------------------------------------    
def SumBestModels(BestDic, BXtest, lev=1 ,label='PLabel', LimitRows=False):          
    
    def Bestreturns(model, p, data ,lev=1):
        X = data.iloc[-p::,::]

        X["output"] = model.predict(X.iloc[::,::])
        X["NormalReturns"] = X["returns"].iloc[:,0].cumsum()

        X["output"] = X["output"].replace(0 , -1) 
        X['outputshift'] = X['output'].shift(1)

        X['strategy'] = X["returns"].iloc[:,0] * X["outputshift"] * lev
        X["StrategyReturns"] = X["strategy"].cumsum()

        return X["StrategyReturns"],X["NormalReturns"]   
    
    best_model_name = max(BestDic, key=lambda key: BestDic[key]["ValACC"]+BestDic[key]["Valprecision"])
    best_model_test_acc = BestDic[best_model_name]['ValACC']
    
    if 'PLabel' in BXtest.columns:
        BXtest = BXtest.drop(['PLabel'] , axis=1)

    ypred = BestDic[best_model_name]['model'].predict(BXtest)
    print('best instrument-model selector is:',best_model_name,'with acc:',best_model_test_acc)
    result_df = pd.concat([BXtest, z], axis=1)
    result_df['PLabel'] = ypred
    
    finaldic = {}  
    symbolreturn = {}
    
    df1 = result_df[result_df['PLabel'] == 1]
    selected_rows = df1[df1['ValProfit %'] > 100]
    
    if LimitRows == False:
        SelectedDF = df1
    else:
        SelectedDF = selected_rows

    for i in range(1,SelectedDF.shape[0]+1):        
        ticker = SelectedDF[i-1:i].index[0][:-2]
        TestData = Maindf[ticker]['Xtest']
        ChosseModel = SelectedDF[i-1:i]['model'].values[0]
        finaldic[ticker+str(i)], symbolreturn[ticker+str(i)] = Bestreturns(ChosseModel, TestData.shape[0], TestData ,lev=lev)   
        
    df1 = pd.DataFrame(finaldic) 
    df2 = pd.DataFrame(symbolreturn)    
    
    df1.fillna(method='ffill', inplace=True)
    df2.fillna(method='ffill', inplace=True)
    
    df1['column_sums'] = df1.sum(axis=1)
    df2['column_sums'] = df2.sum(axis=1)   
    
    return df1 ,df2              
#-----------------------------------------------------------------------------------------------------
def SumBestModelsImp(MainIdf, BestDic, BXtest, z, lev=1 ,label='PLabel', LimitRows=False):          
    
    def Bestreturns(model, p, data ,lev=1):
        X = data.iloc[-p::,::]

        X["output"] = model.predict(X.iloc[::,::])
        X["NormalReturns"] = X["returns"].iloc[:,0].cumsum()

        X["output"] = X["output"].replace(0 , -1) 
        X['outputshift'] = X['output'].shift(1)

        X['strategy'] = X["returns"].iloc[:,0] * X["outputshift"] * lev
        X["StrategyReturns"] = X["strategy"].cumsum()

        return X["StrategyReturns"],X["NormalReturns"]   
    #------------------------------------------------------------------    
    best_model_name = max(BestDic, key=lambda key: BestDic[key]["ValACC"]+BestDic[key]["Valprecision"])
    best_model_test_acc = BestDic[best_model_name]['ValACC']
    
    if 'PLabel' in BXtest.columns:
        BXtest = BXtest.drop(['PLabel'] , axis=1)

    ypred = BestDic[best_model_name]['model'].predict(BXtest)
    print('best instrument-model selector is:',best_model_name,'with acc:',best_model_test_acc)
    result_df = pd.concat([BXtest, z], axis=1)
    result_df['PLabel'] = ypred
    
    finaldic = {}  
    symbolreturn = {}
    
    df1 = result_df[result_df['PLabel'] == 1]
    selected_rows = df1[df1['ValProfit %'] > 100]
    
    if LimitRows == False:
        SelectedDF = df1
    else:
        SelectedDF = selected_rows

    for i in range(1,SelectedDF.shape[0]+1):        
        ticker = SelectedDF[i-1:i].index[0][:-2]
        TestData = MainIdf[ticker]['Xtest']
        ChosseModel = SelectedDF[i-1:i]['model'].values[0]
        finaldic[ticker+str(i)], symbolreturn[ticker+str(i)] = Bestreturns(ChosseModel, TestData.shape[0], TestData ,lev=lev)   
        
    df1 = pd.DataFrame(finaldic) 
    df2 = pd.DataFrame(symbolreturn)    
    
    df1.fillna(method='ffill', inplace=True)
    df2.fillna(method='ffill', inplace=True)
    
    df1['column_sums'] = df1.sum(axis=1)
    df2['column_sums'] = df2.sum(axis=1)   
    
    return df1 ,df2 ,SelectedDF        


# Your New Python File

from AlgorithmImports import *
from lib import *
import warnings
from sklearn.exceptions import UndefinedMetricWarning, ConvergenceWarning
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=ConvergenceWarning)
warnings.filterwarnings("ignore", category=UndefinedMetricWarning)
warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=pd.errors.SettingWithCopyWarning)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import yfinance as yf
import pandas_ta as ta
from IPython.core.display import display, HTML
from collections import Counter
import time
from datetime import datetime

from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC as SupportVectorClassifier
from sklearn.neural_network import MLPClassifier

from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold  
from sklearn.model_selection import StratifiedKFold 
from sklearn.model_selection import GridSearchCV

from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve, roc_auc_score

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class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2023, 1, 1)
        self.SetCash(1000000)
        
        self.symbols = ["AAPL", "MSFT", "GOOG", "AMZN", "FB", "TSLA", "NFLX", "NVDA", "BABA", "JPM", "BND", "SPY"]
        #self. symbols = ["AMZN", "TSLA", "NFLX", "NVDA", "AAPL", "MSFT"]
        self.shifting = 1
        self.instList = []
        self.MainIdf = {}
        qb = QuantBook()

        # Download historical data and set leverage
        self.Log('downloads: -------------------------------------------------------')
        for symbol in self.symbols:
            equity = self.AddEquity(symbol, Resolution.Daily)
            equity.SetLeverage(2)
            history = self.History(equity.Symbol, 2000, Resolution.Daily)
            df = history.loc[symbol]
            df.rename(columns={'open': 'Open', 'high': 'High', 'low': 'Low', 'close': 'Close', 'volume': 'Volume'}, inplace=True)
            self.MainIdf[symbol] = {'DataFrame' : df}  
            self.instList.append((equity.Symbol, symbol))
            self.Log(f"{symbol} : Downloaded")

        self.Log('outputs: -------------------------------------------------------')
        # Load DataFrame from pickle
        import pickle
        import io
        import base64

        if self.ObjectStore.ContainsKey('data1.pkl'):

            base64_str = self.ObjectStore.Read('data1.pkl')
            data = base64.b64decode(base64_str)
            buffer = io.BytesIO(data)
            buffer.seek(0)
            self.SelectedF = pickle.load(buffer)
            self.Debug(f"DataFrame with models loaded successfully: {self.SelectedF}")
        else:
            self.Debug("File not found in Object Store")
    
    
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.At(10, 0), self.PlaceOrders)
        self.Schedule.On(self.DateRules.Tomorrow, self.TimeRules.At(10, 0), self.CloseOrders)

    def OnData(self, data):
        pass
    
    def PlaceOrders(self):
        self.Log('orders: -------------------------------------------------------')
        ordertype = {}
        for i in range(1, self.SelectedF.shape[0] + 1):        
            ticker = self.SelectedF.iloc[i - 1].name[:-2]
            instrument = None
            for j in range(len(self.instList)):
                if self.instList[j][1] == ticker:
                    instrument = self.instList[j][0]
                    self.Log(instrument)
                    break
            
            TData = self.MainIdf[ticker]['DataFrame'][-200:]
            ToData = AddOsil(TData)
            TodData = AddLabel(ToData)
            TodaData, y = TimeSeriesGen(TodData, self.shifting)
            TodayData = TodaData[-1:]
            
            ChosenModel = self.SelectedF.iloc[i - 1]['model']
            order = ChosenModel.predict(TodayData)
            self.Log(order)
            
            if order[0] == 1:
                self.Log(f'{instrument} Buy Order Placed for ticker: {ticker}')
                self.SetHoldings(instrument, 1)  # Long position
                ordertype[ticker + str(i)] = 'Buy'
                    
            elif order[0] == 0:
                self.Log(f'{instrument} Sell Order Placed for ticker: {ticker}')
                self.SetHoldings(instrument, -1)  # Short position
                ordertype[ticker + str(i)] = 'Sell'
            self.Log('---------------------------------------------------------')    
        self.Log('Orders Placement is done.')
    
    def CloseOrders(self):
        self.Log('Closing all positions after 24 hours')
        self.Liquidate()