# The following 10 blocks of code define the classes for storing the the return data, for each
# currency pair.
from math import isnan
from math import sqrt
from math import floor
# from sqlalchemy import create_engine
from sqlalchemy import text
import datetime
# Define the AUDUSD_return class - each instance will store one row from the dataframe
class AUDUSD_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if AUDUSD_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - AUDUSD_return.last_price) / AUDUSD_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            AUDUSD_return.run_sum = 0
        else:
            # Increment the counter
            if AUDUSD_return.num < 5:
                AUDUSD_return.num += 1
            AUDUSD_return.run_sum += hist_return
        AUDUSD_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if not isnan(self.hist_return):
            AUDUSD_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            AUDUSD_return.run_sum -= pop_value
            avg = AUDUSD_return.run_sum / (AUDUSD_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(AUDUSD_return.run_squared_sum / (AUDUSD_return.num))
            self.std_return = std
            AUDUSD_return.run_sum_of_std += std
            AUDUSD_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            AUDUSD_return.run_sum_of_std -= pop_value
            avg_std = AUDUSD_return.run_sum_of_std / (AUDUSD_return.num)
            self.avg_of_std_return = avg_std
            return avg_std

# Define the GBPEUR_return class - each instance will store one row from the dataframe
class GBPEUR_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if GBPEUR_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - GBPEUR_return.last_price) / GBPEUR_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            GBPEUR_return.run_sum = 0
        else:
            # Increment the counter
            if GBPEUR_return.num < 5:
                GBPEUR_return.num += 1
            GBPEUR_return.run_sum += hist_return
        GBPEUR_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            GBPEUR_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            GBPEUR_return.run_sum -= pop_value
            avg = GBPEUR_return.run_sum / (GBPEUR_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(GBPEUR_return.run_squared_sum / (GBPEUR_return.num))
            self.std_return = std
            GBPEUR_return.run_sum_of_std += std
            GBPEUR_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            GBPEUR_return.run_sum_of_std -= pop_value
            avg_std = GBPEUR_return.run_sum_of_std / (GBPEUR_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# Define the USDCAD_return class - each instance will store one row from the dataframe
class USDCAD_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if USDCAD_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - USDCAD_return.last_price) / USDCAD_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            USDCAD_return.run_sum = 0
        else:
            # Increment the counter
            if USDCAD_return.num < 5:
                USDCAD_return.num += 1
            USDCAD_return.run_sum += hist_return
        USDCAD_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            USDCAD_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            USDCAD_return.run_sum -= pop_value
            avg = USDCAD_return.run_sum / (USDCAD_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(USDCAD_return.run_squared_sum / (USDCAD_return.num))
            self.std_return = std
            USDCAD_return.run_sum_of_std += std
            USDCAD_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            USDCAD_return.run_sum_of_std -= pop_value
            avg_std = USDCAD_return.run_sum_of_std / (USDCAD_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# Define the USDJPY_return class - each instance will store one row from the dataframe
class USDJPY_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if USDJPY_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - USDJPY_return.last_price) / USDJPY_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            USDJPY_return.run_sum = 0
        else:
            # Increment the counter
            if USDJPY_return.num < 5:
                USDJPY_return.num += 1
            USDJPY_return.run_sum += hist_return
        USDJPY_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            USDJPY_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            USDJPY_return.run_sum -= pop_value
            avg = USDJPY_return.run_sum / (USDJPY_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(USDJPY_return.run_squared_sum / (USDJPY_return.num))
            self.std_return = std
            USDJPY_return.run_sum_of_std += std
            USDJPY_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            USDJPY_return.run_sum_of_std -= pop_value
            avg_std = USDJPY_return.run_sum_of_std / (USDJPY_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# Define the USDMXN_return class - each instance will store one row from the dataframe
class USDMXN_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if USDMXN_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - USDMXN_return.last_price) / USDMXN_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            USDMXN_return.run_sum = 0
        else:
            # Increment the counter
            if USDMXN_return.num < 5:
                USDMXN_return.num += 1
            USDMXN_return.run_sum += hist_return
        USDMXN_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            USDMXN_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            USDMXN_return.run_sum -= pop_value
            avg = USDMXN_return.run_sum / (USDMXN_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(USDMXN_return.run_squared_sum / (USDMXN_return.num))
            self.std_return = std
            USDMXN_return.run_sum_of_std += std
            USDMXN_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            USDMXN_return.run_sum_of_std -= pop_value
            avg_std = USDMXN_return.run_sum_of_std / (USDMXN_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# Define the EURUSD_return class - each instance will store one row from the dataframe
class EURUSD_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if EURUSD_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - EURUSD_return.last_price) / EURUSD_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            EURUSD_return.run_sum = 0
        else:
            # Increment the counter
            if EURUSD_return.num < 5:
                EURUSD_return.num += 1
            EURUSD_return.run_sum += hist_return
        EURUSD_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            EURUSD_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            EURUSD_return.run_sum -= pop_value
            avg = EURUSD_return.run_sum / (EURUSD_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(EURUSD_return.run_squared_sum / (EURUSD_return.num))
            self.std_return = std
            EURUSD_return.run_sum_of_std += std
            EURUSD_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            EURUSD_return.run_sum_of_std -= pop_value
            avg_std = EURUSD_return.run_sum_of_std / (EURUSD_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# Define the USDCNY_return class - each instance will store one row from the dataframe
class USDCNY_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if USDCNY_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - USDCNY_return.last_price) / USDCNY_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            USDCNY_return.run_sum = 0
        else:
            # Increment the counter
            if USDCNY_return.num < 5:
                USDCNY_return.num += 1
            USDCNY_return.run_sum += hist_return
        USDCNY_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            USDCNY_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            USDCNY_return.run_sum -= pop_value
            avg = USDCNY_return.run_sum / (USDCNY_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(USDCNY_return.run_squared_sum / (USDCNY_return.num))
            self.std_return = std
            USDCNY_return.run_sum_of_std += std
            USDCNY_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            USDCNY_return.run_sum_of_std -= pop_value
            avg_std = USDCNY_return.run_sum_of_std / (USDCNY_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# Define the USDCZK_return class - each instance will store one row from the dataframe
class USDCZK_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if USDCZK_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - USDCZK_return.last_price) / USDCZK_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            USDCZK_return.run_sum = 0
        else:
            # Increment the counter
            if USDCZK_return.num < 5:
                USDCZK_return.num += 1
            USDCZK_return.run_sum += hist_return
        USDCZK_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            USDCZK_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            USDCZK_return.run_sum -= pop_value
            avg = USDCZK_return.run_sum / (USDCZK_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(USDCZK_return.run_squared_sum / (USDCZK_return.num))
            self.std_return = std
            USDCZK_return.run_sum_of_std += std
            USDCZK_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            USDCZK_return.run_sum_of_std -= pop_value
            avg_std = USDCZK_return.run_sum_of_std / (USDCZK_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# Define the USDPLN_return class - each instance will store one row from the dataframe
class USDPLN_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if USDPLN_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - USDPLN_return.last_price) / USDPLN_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            USDPLN_return.run_sum = 0
        else:
            # Increment the counter
            if USDPLN_return.num < 5:
                USDPLN_return.num += 1
            USDPLN_return.run_sum += hist_return
        USDPLN_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            USDPLN_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            USDPLN_return.run_sum -= pop_value
            avg = USDPLN_return.run_sum / (USDPLN_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(USDPLN_return.run_squared_sum / (USDPLN_return.num))
            self.std_return = std
            USDPLN_return.run_sum_of_std += std
            USDPLN_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            USDPLN_return.run_sum_of_std -= pop_value
            avg_std = USDPLN_return.run_sum_of_std / (USDPLN_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# Define the USDINR_return class - each instance will store one row from the dataframe
class USDINR_return(object):
    # Variable to store the total number of instantiated objects in this class
    num = 0
    # Variable to store the running sum of the return
    run_sum = 0
    run_squared_sum = 0
    run_sum_of_std = 0
    last_price = -1

    # Init all the necessary variables when instantiating the class
    def __init__(self, tick_time, avg_price):

        # Store each column value into a variable in the class instance
        self.tick_time = tick_time
        # self.price = avg_price

        if USDINR_return.last_price == -1:
            hist_return = float('NaN')
        else:
            hist_return = (avg_price - USDINR_return.last_price) / USDINR_return.last_price

        self.hist_return = hist_return
        if isnan(hist_return):
            USDINR_return.run_sum = 0
        else:
            # Increment the counter
            if USDINR_return.num < 5:
                USDINR_return.num += 1
            USDINR_return.run_sum += hist_return
        USDINR_return.last_price = avg_price

    def add_to_running_squared_sum(self, avg):
        if isnan(self.hist_return) == False:
            USDINR_return.run_squared_sum += (self.hist_return - avg) ** 2

    def get_avg(self, pop_value):
        if isnan(self.hist_return) == False:
            USDINR_return.run_sum -= pop_value
            avg = USDINR_return.run_sum / (USDINR_return.num)
            self.avg_return = avg
            return avg

    def get_std(self):
        if isnan(self.hist_return) == False:
            std = sqrt(USDINR_return.run_squared_sum / (USDINR_return.num))
            self.std_return = std
            USDINR_return.run_sum_of_std += std
            USDINR_return.run_squared_sum = 0
            return std

    def get_avg_std(self, pop_value):
        if isnan(self.hist_return) == False:
            USDINR_return.run_sum_of_std -= pop_value
            avg_std = USDINR_return.run_sum_of_std / (USDINR_return.num)
            self.avg_of_std_return = avg_std
            return avg_std


# We can buy, sell, or do nothing each time we make a decision.
# This class defies a nobject for keeping track of our current investments/profits for each currency pair
class portfolio(object):
    def __init__(self, from_, to):
        # Initialize the 'From' currency amont to 1
        self.amount = 1
        self.curr2 = 0
        self.from_ = from_
        self.to = to
        # We want to keep track of state, to see what our next trade should be
        self.Prev_Action_was_Buy = False

    # This defines a function to buy the 'To' currency. It will always buy the max amount, in whole number
    # increments
    def buy_curr(self, price):
        if self.amount >= 1:
            num_to_buy = floor(self.amount)
            self.amount -= num_to_buy
            self.Prev_Action_was_Buy = True
            self.curr2 += num_to_buy * price
            print(
                "Bought %d worth of the target currency (%s). Our current profits and losses in the original currency (%s) are: %f." % (
                num_to_buy, self.to, self.from_, (self.amount - 1)))
        else:
            print("There was not enough of the original currency (%s) to make another buy." % self.from_)

    # This defines a function to sell the 'To' currency. It will always sell the max amount, in a whole number
    # increments
    def sell_curr(self, price):
        if self.curr2 >= 1:
            num_to_sell = floor(self.curr2)
            self.amount += num_to_sell * (1 / price)
            self.Prev_Action_was_Buy = False
            self.curr2 -= num_to_sell
            print(
                "Sold %d worth of the target currency (%s). Our current profits and losses in the original currency (%s) are: %f." % (
                num_to_sell, self.to, self.from_, (self.amount - 1)))
        else:
            print("There was not enough of the target currency (%s) to make another sell." % self.to)

# Function slightly modified from polygon sample code to format the date string
def ts_to_datetime(ts) -> str:
    return datetime.datetime.fromtimestamp(ts / 1000.0).strftime('%Y-%m-%d %H:%M:%S')


# Function which clears the raw data tables once we have aggregated the data in a 6 minute interval
def reset_raw_data_tables(engine, currency_pairs):
    with engine.begin() as conn:
        for curr in currency_pairs:
            conn.execute(text("DROP TABLE " + curr[0] + curr[1] + "_raw;"))
            conn.execute(
                text("CREATE TABLE " + curr[0] + curr[1] + "_raw(ticktime text, fxrate  numeric, inserttime text);"))


# This creates a table for storing the raw, unaggregated price data for each currency pair in the SQLite database
def initialize_raw_data_tables(engine, currency_pairs):
    with engine.begin() as conn:
        for curr in currency_pairs:
            conn.execute(
                text("CREATE TABLE " + curr[0] + curr[1] + "_raw(ticktime text, fxrate  numeric, inserttime text);"))


# This creates a table for storing the (6 min interval) aggregated price data for each currency pair in the SQLite database
def initialize_aggregated_tables(engine, currency_pairs):
    with engine.begin() as conn:
        for curr in currency_pairs:
            conn.execute(text(
                "CREATE TABLE " + curr[0] + curr[1] + "_agg(inserttime text, avgfxrate  numeric, stdfxrate numeric);"))


# This function is called every 6 minutes to aggregate the data, store it in the aggregate table,
# and then delete the raw data
def aggregate_raw_data_tables(engine, currency_pairs):
    with engine.begin() as conn:
        for curr in currency_pairs:
            result = conn.execute(
                text("SELECT AVG(fxrate) as avg_price, COUNT(fxrate) as tot_count FROM " + curr[0] + curr[1] + "_raw;"))
            for row in result:
                avg_price = row.avg_price
                tot_count = row.tot_count
            std_res = conn.execute(text(
                "SELECT SUM((fxrate - " + str(avg_price) + ")*(fxrate - " + str(avg_price) + "))/(" + str(
                    tot_count) + "-1) as std_price FROM " + curr[0] + curr[1] + "_raw;"))
            for row in std_res:
                std_price = sqrt(row.std_price)
            date_res = conn.execute(text("SELECT MAX(ticktime) as last_date FROM " + curr[0] + curr[1] + "_raw;"))
            for row in date_res:
                last_date = row.last_date
            conn.execute(text("INSERT INTO " + curr[0] + curr[
                1] + "_agg (inserttime, avgfxrate, stdfxrate) VALUES (:inserttime, :avgfxrate, :stdfxrate);"),
                         [{"inserttime": last_date, "avgfxrate": avg_price, "stdfxrate": std_price}])

            # This calculates and stores the return values
            exec("curr[2].append(" + curr[0] + curr[1] + "_return(last_date,avg_price))")
            # exec("print(\"The return for "+curr[0]+curr[1]+" is:"+str(curr[2][-1].hist_return)+" \")")

            if len(curr[2]) > 5:
                try:
                    avg_pop_value = curr[2][-6].hist_return
                except:
                    avg_pop_value = 0
                if isnan(avg_pop_value) == True:
                    avg_pop_value = 0
            else:
                avg_pop_value = 0
            # Calculate the average return value and print it/store it
            curr_avg = curr[2][-1].get_avg(avg_pop_value)
            # exec("print(\"The average return for "+curr[0]+curr[1]+" is:"+str(curr_avg)+" \")")

            # Now that we have the average return, loop through the last 5 rows in the list to start compiling the
            # data needed to calculate the standard deviation
            for row in curr[2][-5:]:
                row.add_to_running_squared_sum(curr_avg)

            # Calculate the standard dev using the avg
            curr_std = curr[2][-1].get_std()
            # exec("print(\"The standard deviation of the return for "+curr[0]+curr[1]+" is:"+str(curr_std)+" \")")

            # Calculate the average standard dev
            if len(curr[2]) > 5:
                try:
                    pop_value = curr[2][-6].std_return
                except:
                    pop_value = 0
            else:
                pop_value = 0
            curr_avg_std = curr[2][-1].get_avg_std(pop_value)
            # exec("print(\"The average standard deviation of the return for "+curr[0]+curr[1]+" is:"+str(curr_avg_std)+" \")")

            # -------------------Investment Strategy-----------------------------------------------
            try:
                return_value = curr[2][-1].hist_return
            except:
                return_value = 0
            if isnan(return_value) == True:
                return_value = 0

            try:
                return_value_1 = curr[2][-2].hist_return
            except:
                return_value_1 = 0
            if isnan(return_value_1) == True:
                return_value_1 = 0

            try:
                return_value_2 = curr[2][-3].hist_return
            except:
                return_value_2 = 0
            if isnan(return_value_2) == True:
                return_value_2 = 0

            try:
                upp_band = curr[2][-1].avg_return + (1.5 * curr[2][-1].std_return)
                if return_value >= upp_band and curr[
                    3].Prev_Action_was_Buy == True and return_value != 0:  # (return_value > 0) and (return_value_1 > 0) and
                    curr[3].sell_curr(avg_price)
            except:
                pass

            try:
                loww_band = curr[2][-1].avg_return - (1.5 * curr[2][-1].std_return)
                if return_value <= loww_band and curr[
                    3].Prev_Action_was_Buy == False and return_value != 0:  # and  (return_value < 0) and (return_value_1 < 0)
                    curr[3].buy_curr(avg_price)
            except:
                pass