Dollar Averaging Strategy

dollar_averaging.py

def dollar_averaging(df, trade_date, trade_amount, f_or_b = 'f'):
    
    '''
    Input:
    1. df (dataframe)
        ---> Datetime object should be set as index

    2. trade_date (int)
        ---> determine which date to invest

    3. trade_amount (int)
        ---> determine the amount of investing

    4. f_or_b (str)
        ---> once trade_date market closed, move forward or backward

    Output:
    1. profit_or_loss (float)
        ---> dollar-averaging strategy profit/loss

    2. annualized_ret
        ---> dollar-averaging strategy annualized return

    3. trade_detail
        ---> trading record during the data period
    '''

    date_in_date = list(set([(date.year, date.month) for date in df.index]))
    date_in_date = sorted(list(date_in_date), key=lambda x: (x[0], x[1]))
    
    date_for_investing = trade_date
    f_or_b = 1 if f_or_b == 'f' else 0
    
    searched_count = 0
    purchasing_date_list = []

    for ym in date_in_date:

        current_year = ym[0]
        current_month = ym[1]
        current_day = date_for_investing
        purchasing_date = 'NA'

        while purchasing_date == 'NA':
            
            try:

                searching_date = datetime.strptime(str(current_year) + '-' +
                                                   str(current_month)+ '-' +
                                                   str(current_day), '%Y-%m-%d')

                if searching_date in df.index:

                    purchasing_date = searching_date
                    purchasing_date_list.append(purchasing_date)

                else:
                    current_day += 1
                    searched_count += f_or_b # if selected date is not a trading date, then move backward or foreward
            
            # if current_day add more than valid date for current month
            except ValueError:
                break
            
    # initial parameters for dollar averaging
    da_amount = int(trade_amount)
    shares = 0
    total_cost = 0
    trade_detail = {}
    
    # create trading details
    for date in purchasing_date_list:
        
        cost_price = df.loc[date][0]
        got_shares = int(da_amount / cost_price)
        cost = round(cost_price * got_shares) * (1 + 0.001425*0.6)

        trade_detail[date] = [cost_price, got_shares, cost]
        shares += got_shares
        total_cost += cost
        
    # Count days between first and the last time trading for Annualize
    da_period_as_year = (purchasing_date_list[-1] - purchasing_date_list[0]).days / 365

    # Calculate dollar-averaging performance
    profit_or_loss = round( (shares * df.loc[purchasing_date_list[-1]][0]) - total_cost )
    annualized_ret = round((profit_or_loss / total_cost + 1) ** (1 / da_period_as_year) - 1, 4)
    
    return profit_or_loss, annualized_ret, trade_detail