Contrarian_Psychological_Levels.py

# Base parameters
expected_cost    = 0.0 * (lot / 100000) 
assets           = asset_list(1) 
window           = 1000

# Trading parameters   
horizon          = 'H1'

# Indicator parameters
trend = 20

# Mass imports 
my_data = mass_import(0, horizon)

# Signal
def signal(Data):
    
    for i in range(len(Data)):
        if Data[i, 4] == 1 and Data[i, 3] < Data[i - trend, 3]:
            Data[i, 6] = 1
        elif Data[i, 4] == 1 and Data[i, 3] > Data[i - trend, 3]:
            Data[i, 7] = -1
    return Data
##############################################################################   1
my_data = rounding(my_data, 4)
my_data = psychological_levels_scanner(my_data, trend, 4, 6, 7)
my_data = adder(my_data, 10)
my_data = signal(my_data)

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])

if sigchart == True:
    signal_chart_ohlc_color(my_data, assets[0], 3, 6, 7, window = 500)   
    indicator_plot_double(my_data, 0, 1, 2, 3, 4, window = 250)        
    plt.axhline(y = upper_barrier, color = 'black', linewidth = 1, linestyle = '--')
    plt.axhline(y = lower_barrier, color = 'black', linewidth = 1, linestyle = '--')    

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