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import numpy as np
import pandas as pd
import statsmodels.api as sm
 
data = pd.read_csv("http://web.pdx.edu/~crkl/ceR/data/usyc87.txt",index_col='YEAR',sep='\s+',nrows=66)
y = data['Y']
c = data['C']
 
from statsmodels.tsa.vector_ar.vecm import coint_johansen
 

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import matplotlib.pyplot as plt
import pandas as pd
from johansen import coint_johansen
 
df_x = pd.read_csv ("yourfile.csv",index_col=0)
df_y = pd.read_csv ("yourfile.csv",index_col=0)
 
df = pd.DataFrame({'x':df_x['Close'],'y':df_y['Close']})
 
coint_johansen(df,0,1)

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from datetime import timedelta

class IronCondorAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 2, 1)
        self.SetEndDate(2021, 8, 1)
        self.SetCash(30000)
        equity = self.AddEquity("TSLA", Resolution.Minute)
        option = self.AddOption("TSLA", Resolution.Minute)

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companies_dict = {
    'Amazon':'AMZN',
    'Apple':'AAPL',
    'Walgreen':'WBA',
    'Northrop Grumman':'NOC',
    'Boeing':'BA',
    'Lockheed Martin':'LMT',
    'McDonalds':'MCD',
    'Intel':'INTC',
    'Navistar':'NAV',

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#requirements 
#alpaca-trade-api>=0.25
#ta
#sklearn

import alpaca_trade_api as tradeapi
import requests
import time
from ta import macd
import numpy as np

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# Step 1: Load libraries and data
library(quantmod)
library(PerformanceAnalytics)
## 
##Attaching package: 'PerformanceAnalytics'
##The following object is masked from 'package:graphics':
##legend
getSymbols('NFCI', src = 'FRED', , from = '2000-01-01')
##[1] "NFCI"
NFCI <- na.omit(lag(NFCI)) # we can only act on the signal after release, i.e. the next day

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# The investment universe consists of 24 commodity futures, 12 cross-currency pairs (with 9 underlying currencies), 9 developed equity indices, and 13 
# government bond futures.
# Every month, the investor considers whether the excess return of each asset over the past 12 months is positive or negative and goes long on the 
# contract if it is positive and short if negative. The position size is set to be inversely proportional to the instrument’s volatility. A univariate 
# GARCH model is used to estimated  ex-ante volatility in the source paper. However, other simple models could probably be easily used with good results 
# (for example, the easiest one would be using historical volatility instead of estimated volatility). The portfolio is rebalanced monthly.
# QC implementation changes:
# instead of GARCH model volatility, simple historical volatility is used in this code. 
# for more info on trading strategies visit miltonfmr.com
            

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# Load libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas import read_csv, set_option
from pandas.plotting import scatter_matrix
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV
from sklearn.linear_model import LogisticRegression

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#Scikit-learn library to build and train a linear regression model
# First, we'll import the necessary libraries
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# Next, we'll load our data and split it into training and testing sets
X = # Your input data (economic indicators)
y = # Your output data (stagflation probability)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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#Markov-Switching Vector Autoregression (MSVAR) model
import numpy as np
import statsmodels.tsa.api as smt

# Set the number of lags to consider
nlags = 4

# Create the model
model = smt.MSVAR(data, lags=nlags)