import requests # this lets us call the API to the external website
import pandas as pd

def getdata(stock: str):
    """This function gets data from the API and returns specific values
    :param stock: Official stock ticker symbol
    
    :returns share_price: Array of current share price
    :returns cash: Array of cash on hand 
    :returns debt: Array of debt
    :returns qRev: Array of quarterly revenue
    :returns ceo: Name of the CEO 
    """
    # Company Quote Group of Items
    company_quote = requests.get(f"{base}quote/{stock}?apikey={key}")
    company_quote = company_quote.json()
    share_price = float(company_quote[0]['price'])

    # Balance Sheet Group of Items
    BS = requests.get(f"{base}financials/balance-sheet-statement/{stock}?period=quarter&apikey={key}")
    BS = BS.json()

    # Total Cash
    cash = float(BS['financials'][0]['Cash and short-term investments'])
    # Total Debt
    debt = float(BS['financials'][0]['Total debt'])

    # Income Statement Group of Items
    IS = requests.get(f"{base}financials/income-statement/{stock}?period=quarter&apikey={key}")
    IS = IS.json()

    # Most Recent Quarterly Revenue
    qRev = float(IS['financials'][0]['Revenue'])

    # Company Profile Group of Items
    company_info = requests.get(f"{base}company/profile/{stock}?apikey={key}")
    company_info = company_info.json()

    # Chief Executive Officer
    ceo = company_info['profile']['ceo']

    return (share_price, cash, debt, qRev, ceo)

base = 'https://financialmodelingprep.com/api/v3/'
key = '<YOUR KEY HERE>'
tickers = ('AAPL', 'MSFT', 'GOOG', 'T', 'CSCO', 'INTC', 'ORCL', 'AMZN', 'FB', 'TSLA', 'NVDA')
    
data = map(getdata, tickers)   

quarter = '1Q2020'

df = pd.DataFrame(data,
                    columns=['Share Price ($)', 'Total Cash', 'Total Debt', f'{quarter} Revenue', 'CEO'],
                    index=tickers)

writer = pd.ExcelWriter('example.xlsx')
df.to_excel(writer, 'Statistics')
writer.save()

df.head(10)

# convert to billions for easier reading

cols_to_convert = ['Total Cash','Total Debt','1Q2020 Revenue']

df[cols_to_convert] = df[cols_to_convert] / 10**9

df.head(10)

# getting historical data for RDS-A. This code calls the API and transforms the result into a DataFrame.
import pandas as pd
from pandas.io.json import json_normalize 

df = pd.read_json("https://financialmodelingprep.com/api/v3/quotes/nyse?apikey=<YOUR KEY HERE>")

df.head()

key = '<YOUR KEY HERE>'
ticker = "T"
target = "https://financialmodelingprep.com/api/v3/historical-price-full/{}?apikey={}".format(ticker, key)
df = pd.read_json(target)
df = json_normalize(df['historical'])
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

df.to_csv('{} data.csv'.format(ticker))

df = pd.read_csv('data/RDS-A data.csv')
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
ticker = "RDS-A"

df.head()

%matplotlib inline

df['adjClose'].plot()

import matplotlib.pyplot as plt
%matplotlib inline

def bollinger_bands(df, target_col: str = 'adjClose')->pd.DataFrame:
    """Calculates Bollinger Bands and returns an updated DataFrame. 

    :param df: DataFrame
    :param target_col: column that will be used for the calcuations
    :type target_col: str

    :return df: df with additional columns
    :rtype df: pd.DataFrame
    """
    
    df['20 Day MA'] = df[target_col].rolling(20).mean()
    df['20 Day MA_lower bound'] = df['20 Day MA'] - df[target_col].rolling(20).std()*2
    df['20 Day MA_upper bound'] = df['20 Day MA'] + df[target_col].rolling(20).std()*2
    
    return df

def bb_plot(df: pd.DataFrame = df, target_col: str = 'adjClose'):
    """Calculates time series plot with Bollinger Bands 

    :param df: DataFrame
    :param target_col: column that will be used for the calcuations
    :type target_col: str

    :return: plot
    :rtype: matplotlib.pyplot
    """
    
    x = df.index
    y= df[['adjClose', '20 Day MA', '20 Day MA_lower bound','20 Day MA_upper bound']]

    plt.fill_between(x, rds['20 Day MA_lower bound'],rds['20 Day MA_upper bound'], alpha = .5)
    plt.plot(x,y)
    plt.title("Bollinger Bands for {}".format(ticker))
    plt.xlabel('Date (Year/Month)')
    plt.ylabel('Price(USD)')
    plt.legend(y)
    plt.show()
    
    return plt

rds = bollinger_bands(df.sort_values(by='date'), 'adjClose')
bb_plot(rds)

import numpy as np

def bb_strategy(df: pd.DataFrame)->pd.DataFrame:
    """Calculates the returns of implementing the Bollinger Bands 

    :param df: DataFrame

    :return df: df with additional columns for Positing, Market Return, and Strategy Return
    :rtype df: pd.DataFrame
    """
    df['Position'] = None
    #Fill our newly created position column - set to sell (-1) when the price hits the upper band, and set to buy (1) when it hits the lower band
    for row in range(len(df)):
    
        if (df['adjClose'].iloc[row] > df['20 Day MA_upper bound'].iloc[row]) and (df['adjClose'].iloc[row-1] < df['20 Day MA_upper bound'].iloc[row-1]):
            df['Position'].iloc[row] = -1
        
        if (df['adjClose'].iloc[row] < df['20 Day MA_lower bound'].iloc[row]) and (df['adjClose'].iloc[row-1] > df['20 Day MA_lower bound'].iloc[row-1]):
            df['Position'].iloc[row] = 1  

    #Forward fill our position column to replace the "None" values with the correct long/short positions to represent the "holding" of our position
    #forward through time
    df['Position'].fillna(method='ffill',inplace=True)

    #Calculate the daily market return and multiply that by the position to determine strategy returns
    df['Market Return'] = np.log(df['adjClose'] / df['adjClose'].shift(1))
    df['Strategy Return'] = df['Market Return'] * df['Position']
    
    return df

rds = bb_strategy(rds)
rds['Strategy Return'].cumsum().plot()