Pattern_TD_Waldo_2.py

# Base parameters
expected_cost    = 0.5 * (lot / 10000) 
assets           = asset_list(1) 
window           = 1000

# Trading parameters   
horizon          = 'H1'

# Mass imports 
my_data = mass_import(0, horizon)

def signal(Data, high, low, close, buy, sell):    
    # Adding a few columns
    Data = adder(Data, 10)
    for i in range(len(Data)):
        # Short-term Bottom
        if Data[i, 2] < Data[i - 1, 2] and Data[i, 2] < Data[i - 2, 2] and Data[i, 2] < Data[i - 3, 2] and Data[i, 2] < Data[i - 4, 2] and Data[i, 3] > Data[i - 1, 3] and Data[i, 3] > Data[i - 2, 3] and Data[i, 3] > Data[i - 3, 3] and Data[i, 3] > Data[i - 4, 3]:
               Data[i, buy] = 1
        # Short-term Top
        if Data[i, 1] > Data[i - 1, 1] and Data[i, 1] > Data[i - 2, 1] and Data[i, 1] > Data[i - 3, 1] and Data[i, 1] > Data[i - 4, 1] and Data[i, 3] < Data[i - 1, 3] and Data[i, 3] < Data[i - 2, 3] and Data[i, 3] < Data[i - 3, 3] and Data[i, 3] < Data[i - 4, 3]:
               Data[i, sell] = -1
    return Data

          
##############################################################################   1

my_data = adder(my_data, 10)
my_data = signal(my_data, 1, 2, 3, 6, 7)

if sigchart == True:
    signal_chart_ohlc_color(my_data, assets[0], 3, 6, 7, window = 250)

holding(my_data, 6, 7, 8, 9)
my_data_eq = equity_curve(my_data, 8, expected_cost, lot, investment)
performance(my_data_eq, 8, my_data, assets[0])  

plt.plot(my_data_eq[:, 3], linewidth = 1, label = assets[0])
plt.grid()
plt.legend()
plt.axhline(y = investment, color = 'black', linewidth = 1)