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v2_hyperparameters_model_passed_saved.py
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v2_hyperparameters_model_passed_saved.py
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# V2 models
# forex_pairs = [
# 'AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'AUDUSD',
# 'CADCHF', 'CADJPY',
# 'CHFJPY',
# 'EURAUD', 'EURCAD', 'EURCHF', 'EURGBP',
# 'EURJPY', 'EURNZD', 'EURUSD',
# 'GBPAUD', 'GBPCAD', 'GBPCHF',
# 'GBPJPY', 'GBPUSD', 'GBPNZD',
# 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD',
# 'USDCHF', 'USDCAD', 'USDJPY'
# ]
# works when period = 45 in harmonic mean low and harmonic mean high in day candles
# audcad audusd
test_model(symbol="USDJPY", iteration=3, learning_rate=0.2, depth=12, window_size=7, save_model=True) # version A passed
# test_model(symbol="USDJPY", iteration=3, learning_rate=0.23, depth=12, window_size=7) # version A
test_model(symbol="EURAUD", iteration=33, learning_rate=0.15, depth=10, window_size=8, save_model=True) # version -> A passed
# test_model(symbol="EURAUD", iteration=7, learning_rate=0.4, depth=7, window_size=8) # version passed
# test_model(symbol="EURAUD", iteration=3, learning_rate=0.15, depth=11, window_size=11) # version
# xxtest_model(symbol="EURAUD", iteration=27, learning_rate=0.19, depth=10, window_size=8) # another version -> A
# test_model(symbol="EURAUD", iteration=27, learning_rate=0.15, depth=10, window_size=8) # another version -> A
# test_model(symbol="EURAUD", iteration=77, learning_rate=0.5, depth=9, window_size=10) # version
# test_model(symbol="EURAUD", iteration=100, learning_rate=0.05, depth=6, window_size=9) # version
test_model(symbol="EURNZD", iteration=11, learning_rate=0.15, depth=9, window_size=9, save_model=True) # version -> A passed
# test_model(symbol="EURNZD", iteration=9, learning_rate=0.25, depth=10, window_size=9) # version -> A
# Pair EURNZD__________________________experimental
# test_model(symbol="EURNZD", iteration=5, learning_rate=0.15, depth=10, window_size=6) # another version
# test_model(symbol="EURNZD", iteration=150, learning_rate=0.02, depth=6, window_size=7) # another version
# test_model(symbol="EURNZD", iteration=66, learning_rate=0.51, depth=10, window_size=9) # another version
# test_model(symbol="EURNZD", iteration=300, learning_rate=0.01, depth=6, window_size=7) # another version
test_model(symbol="EURCAD", iteration=28, learning_rate=0.17, depth=7, window_size=6, alpha_type="beta", save_model=True ) # version A passed
# test_model(symbol="EURCAD", iteration=21, learning_rate=0.25, depth=8, window_size=9) # version A passed
# test_model(symbol="EURCAD", iteration=27, learning_rate=0.17, depth=7, window_size=6, alpha_type="beta" ) # version seems passed
# Pair EURCAD___________________experimental
# test_model(symbol="EURCAD", iteration=1, learning_rate=0.10, depth=10, window_size=9) # version
# xxtest_model(symbol="EURCAD", iteration=50, learning_rate=0.05, depth=6, window_size=6, alpha_type="gamma" ) # version A gamma
# test_model(symbol="EURCAD", iteration=13, learning_rate=0.05, depth=9, window_size=7, alpha_type="alpha" ) # version A alpha
# xx test_model(symbol="EURCAD", iteration=5, learning_rate=0.10, depth=10, window_size=9, alpha_type="gamma") # another version A
# test_model(symbol="EURCAD", iteration=22, learning_rate=0.25, depth=8, window_size=9) # version seems passed
test_model(symbol="EURUSD", iteration=210, learning_rate=0.05, depth=6, window_size=7, alpha_type="gamma", save_model=True ) # version A passed
# test_model(symbol="EURUSD", iteration=5, learning_rate=0.05, depth=6, window_size=7, alpha_type="gamma" ) # another version
# test_model(symbol="EURUSD", iteration=1, learning_rate=0.25, depth=10, window_size=9, alpha_type="alpha" ) # another version
test_model(symbol="USDCAD", iteration=8, learning_rate=0.11, depth=11, window_size=6, alpha_type="delta", save_model=True) # version A passed
# ______
# experimental
# test_model(symbol="USDCAD", iteration=3, learning_rate=0.11, depth=9, window_size=9, alpha_type="omega") # another version
# test_model(symbol="USDCAD", iteration=1, learning_rate=0.20, depth=10, window_size=9, alpha_type="omega") # another version
# xxtest_model(symbol="USDCAD", iteration=5, learning_rate=0.5, depth=10, window_size=11, alpha_type="gamma") # another version
# xxtest_model(symbol="USDCAD", iteration=5, learning_rate=0.15, depth=10, window_size=11, alpha_type="gamma") # version A 60 accuracy
# test_model(symbol="USDCAD", iteration=7, learning_rate=0.11, depth=11, window_size=6, alpha_type="delta") # version passed
test_model(symbol="NZDUSD", iteration=11, learning_rate=0.25, depth=10, window_size=7, alpha_type="gamma", save_model=True) # version A passed
# test_model(symbol="NZDUSD", iteration=77, learning_rate=0.21, depth=6, window_size=6, alpha_type="gamma") # version A
# test_model(symbol="NZDUSD", iteration=9, learning_rate=0.25, depth=10, window_size=7, alpha_type="gamma") # version A
# _____________
# experimental
# test_model(symbol="NZDUSD", iteration=1, learning_rate=0.3, depth=7, window_size=7, alpha_type="alpha") # version
# test_model(symbol="NZDUSD", iteration=3, learning_rate=0.7, depth=11, window_size=7, alpha_type="gamma") # version
# test_model(symbol="NZDUSD", iteration=11, learning_rate=0.2, depth=10, window_size=7, alpha_type="gamma") # version
# test_model(symbol="NZDUSD", iteration=17, learning_rate=0.29, depth=10, window_size=7, alpha_type="gamma") # another version
# test_model(symbol="NZDUSD", iteration=21, learning_rate=0.21, depth=6, window_size=7, alpha_type="gamma") # another version
# test_model(symbol="NZDUSD", iteration=3, learning_rate=0.15, depth=9, window_size=7, alpha_type="gamma") # another version
test_model(symbol="GBPUSD", iteration=19, learning_rate=0.19, depth=9, window_size=11, alpha_type="gamma", save_model=True) # another version A passed
# _________
# experimental
# test_model(symbol="GBPUSD", iteration=3, learning_rate=0.25, depth=11, window_size=6, alpha_type="delta") # version
# test_model(symbol="GBPUSD", iteration=17, learning_rate=0.25, depth=11, window_size=9, alpha_type="delta") # another version
# test_model(symbol="GBPUSD", iteration=5, learning_rate=0.15, depth=10, window_size=11, alpha_type="gamma") # version A
# test_model(symbol="GBPUSD", iteration=7, learning_rate=0.19, depth=9, window_size=11, alpha_type="gamma") # another version A
# test_model(symbol="GBPUSD", iteration=9, learning_rate=0.21, depth=9, window_size=11, alpha_type="omega") # another version A
test_model( symbol="GBPJPY", iteration=10, learning_rate=0.1, depth=10, window_size=5, alpha_type="lambda", save_model=True) # version A passed
# test_model( symbol="GBPJPY", iteration=27, learning_rate=0.27, depth=7, window_size=8, alpha_type="beta") # version A passed
# x test_model(symbol="GBPJPY", iteration=8, learning_rate=0.17, depth=11, window_size=13, alpha_type="delta") # version seems passed
# x test_model(symbol="GBPJPY", iteration=3, learning_rate=0.35, depth=11, window_size=6, alpha_type="alpha", save_model=True) # version passed close to A
# x test_model(symbol="GBPJPY", iteration=7, learning_rate=0.35, depth=11, window_size=6, alpha_type="alpha") # version A passed
# _______________
# experimental
# test_model(symbol="GBPJPY", iteration=7, learning_rate=0.15, depth=9, window_size=13, alpha_type="gamma") # another version accuracy by net gains 54-57
# test_model(symbol="GBPJPY", iteration=2, learning_rate=0.7, depth=13, window_size=6, alpha_type="alpha") # another version
# test_model(symbol="GBPJPY", iteration=5, learning_rate=0.3, depth=10, window_size=7, alpha_type="alpha") # another version
# test_model(symbol="GBPJPY", iteration=5, learning_rate=0.3, depth=10, window_size=7, alpha_type="alpha") # another version
# test_model(symbol="GBPJPY", iteration=7, learning_rate=0.6, depth=12, window_size=11, alpha_type="gamma") # another version
# test_model(symbol="GBPJPY", iteration=3, learning_rate=0.7, depth=12, window_size=6, alpha_type="alpha") # another version close to A
# test_model(symbol="GBPJPY", iteration=3, learning_rate=0.19, depth=10, window_size=3, alpha_type="delta") # another version
# test_model(symbol="GBPAUD", iteration=100, learning_rate=0.05, depth=6, window_size=9) # version seems passed
test_model(symbol="GBPAUD", iteration=12, learning_rate=0.07, depth=5, window_size=6,alpha_type="gamma", save_model=True) # version A passed
test_model(symbol="AUDJPY", iteration=1, learning_rate=0.2, depth=11, window_size=7, alpha_type="delta", save_model=True) # version A delta passed
# _________
# experimental
# test_model(symbol="AUDJPY", iteration=1, learning_rate=0.25, depth=11, window_size=7, alpha_type="delta") # version A delta passed
# test_model(symbol="AUDJPY", iteration=27, learning_rate=0.2, depth=10, window_size=11, alpha_type="omega") # version omega
# test_model(symbol="AUDJPY", iteration=1, learning_rate=0.2, depth=11, window_size=9, alpha_type="alpha") # version A
# test_model(symbol="AUDJPY", iteration=3, learning_rate=0.7, depth=12, window_size=6, alpha_type="gamma") # another version
# test_model(symbol="AUDJPY", iteration=1, learning_rate=0.01, depth=9, window_size=9, alpha_type="alpha") # another version
# test_model(symbol="AUDJPY", iteration=5, learning_rate=0.15, depth=13, window_size=7, alpha_type="alpha") # another version
test_model(symbol="EURJPY", iteration=5, learning_rate=0.2, depth=11, window_size=5, alpha_type="gamma", save_model=True) # version A passed
# test_model(symbol="EURJPY", iteration=5, learning_rate=0.2, depth=9, window_size=7, alpha_type="delta") # another version
# test_model(symbol="EURJPY", iteration=7, learning_rate=0.5, depth=5, window_size=11, alpha_type="gamma") # another version
# test_model(symbol="AUDNZD", iteration=13, learning_rate=0.5, depth=9, window_size=9, alpha_type="gamma") # version seems passed
# test_model(symbol="AUDNZD", iteration=5, learning_rate=0.15, depth=11, window_size=7, alpha_type="beta") # version A seems passed
test_model(symbol="AUDNZD", iteration=9, learning_rate=0.03, depth=10, window_size=6, alpha_type="beta", save_model=True) # version A passed
test_model(symbol="GBPNZD", iteration=12, learning_rate=0.25, depth=10, window_size=7, alpha_type="beta", save_model=True) # version A passed
# test_model(symbol="GBPNZD", iteration=15, learning_rate=0.25, depth=10, window_size=7, alpha_type="beta") # version A seems passed
test_model(symbol="AUDUSD", iteration=14, learning_rate=0.21, depth=7, window_size=9, alpha_type="beta", save_model=True) # version A seems passed
# ______
# test_model(symbol="AUDUSD", iteration=33, learning_rate=0.15, depth=12, window_size=5, alpha_type="beta") # version A seems passed
# test_model(symbol="AUDUSD", iteration=3, learning_rate=0.1, depth=10, window_size=11, alpha_type="delta") # version seems passed
# test_model(symbol="AUDUSD", iteration=23, learning_rate=0.25, depth=8, window_size=7, alpha_type="gamma", save_model=True) # version A passed
test_model(symbol="NZDJPY", iteration=11, learning_rate=0.35, depth=11, window_size=7, alpha_type="beta", save_model=True) # version A passed
# test_model(symbol="USDCHF", iteration=3, learning_rate=0.25, depth=12, window_size=6, alpha_type="gamma") # version passed close to A
# test_model(symbol="USDCHF", iteration=17, learning_rate=0.15, depth=7, window_size=11, alpha_type="beta") # version passed
# test_model(symbol="USDCHF", iteration=7, learning_rate=0.22, depth=8, save_model=False, window_size=6, alpha_type="ha" ) # version passed
test_model(symbol="USDCHF", iteration=6, learning_rate=0.22, depth=8, window_size=6, alpha_type="ha", save_model=True) # version passed close to A
test_model(symbol="AUDCAD", iteration=23, learning_rate=0.25, depth=8, window_size=5, alpha_type="gamma", save_model=True) # # version passed A
# test_model(symbol="CHFJPY", iteration=11, learning_rate=0.75, depth=8, window_size=5, alpha_type="alpha" ) # version close to A
test_model(symbol="CHFJPY", iteration=21, learning_rate=0.75, depth=8, window_size=5, alpha_type="alpha" , save_model=True) # version A
test_model(symbol="GBPCAD", iteration=13, learning_rate=0.25, depth=8, window_size=7, alpha_type="alpha", save_model=True) # version passed A
# test_model(symbol="GBPCAD", iteration=15, learning_rate=0.25, depth=8, window_size=7, alpha_type="alpha") # version passed close to A
test_model(symbol="NZDCHF", iteration=22, learning_rate=0.77, depth=7, window_size=5, alpha_type="beta", save_model=True) # version A passed
test_model(symbol="EURGBP", iteration=14, learning_rate=0.18, depth=9, window_size=6, alpha_type="omega", save_model=True) # version A passed
# test_model(symbol="EURGBP", iteration=15, learning_rate=0.18, depth=9, window_size=6, alpha_type="omega") # version A passed
# test_model(symbol="EURGBP", iteration=7, learning_rate=0.77, depth=8, window_size=7, alpha_type="gamma") # version close to A
# ______________________________________
test_model(symbol="CADCHF", iteration=14, learning_rate=0.20, depth=7, window_size=7, alpha_type="omega", save_model=True) # version A passed
# test_model(symbol="CADCHF", iteration=15, learning_rate=0.18, depth=8, window_size=6, alpha_type="beta") # version close to A
# test_model(symbol="CADCHF", iteration=14, learning_rate=0.20, depth=7, window_size=7, alpha_type="beta") # version close to A
test_model(symbol="AUDCHF", iteration=25, learning_rate=0.25, depth=7, window_size=11, alpha_type="delta", save_model=True) # version A passed
# test_model(symbol="AUDCHF", iteration=22, learning_rate=0.25, depth=7, window_size=11, alpha_type="delta") # version close to A
# test_model(symbol="AUDCHF", iteration=27, learning_rate=0.66, depth=8, window_size=6, alpha_type="alpha") # version close to A
test_model(symbol="GBPCHF", iteration=13, learning_rate=0.42, depth=7, window_size=5, alpha_type="beta", save_model=True) # version A passed
# test_model(symbol="GBPCHF", iteration=13, learning_rate=0.49, depth=7, window_size=5, alpha_type="beta") # version close to A
# test_model(symbol="GBPCHF", iteration=12, learning_rate=0.42, depth=7, window_size=5, alpha_type="beta") # version
test_model(symbol="EURCHF", iteration=20, learning_rate=0.18, depth=7, window_size=5, alpha_type="omega", save_model=True) # version A passed
# test_model(symbol="EURCHF", iteration=19, learning_rate=0.18, depth=7, window_size=5, alpha_type="omega") # version close to A
# test_model(symbol="EURCHF", iteration=12, learning_rate=0.44, depth=7, window_size=5, alpha_type="beta") # version close to A
test_model( symbol="CADJPY", iteration=7, learning_rate=0.38, depth=9, window_size=11, alpha_type="delta", save_model=True) # version A passed
# test_model( symbol="CADJPY", iteration=7, learning_rate=0.71, depth=12, window_size=6, alpha_type="beta" ) version close to A
# test_model( symbol="CADJPY", iteration=47, learning_rate=0.47, depth=9, window_size=5, alpha_type="beta" ) # version A passed
test_model( symbol="NZDCAD", iteration=7, learning_rate=0.68, depth=11, window_size=6, alpha_type="gamma", save_model=True) # version A passed
# test_model( symbol="NZDCAD", iteration=7, learning_rate=0.2, depth=12, window_size=9, alpha_type="omega" )
# test_model( symbol="NZDCAD", iteration=7, learning_rate=0.2, depth=12, window_size=9, alpha_type="gamma" ) # version close to A
# test_model( symbol="NZDCAD", iteration=6, learning_rate=0.35, depth=11, window_size=6, alpha_type="gamma" ) # version close to A
# test_model( symbol="NZDCAD", iteration=7, learning_rate=0.36, depth=11, window_size=6, alpha_type="gamma" ) # version close to A
# __________
# experimental
# test_model(symbol="USDCHF", iteration=4, learning_rate=0.18, depth=8, window_size=5, alpha_type="beta") # version passed
# test_model(symbol="USDCHF", iteration=3, learning_rate=0.78, depth=13, window_size=7, alpha_type="omega") # version passed close to A
# test_model(symbol="USDCHF", iteration=3, learning_rate=0.78, depth=11, window_size=5, alpha_type="omega") # version passed close to A
# test_model(symbol="USDCHF", iteration=7, learning_rate=0.15, depth=8, window_size=7, alpha_type="ha") # version passed close to A
# test_model(symbol="USDCHF", iteration=3, learning_rate=0.58, depth=11, window_size=5, alpha_type="omega") # version passed
# Day Features Settings Adjusted
hl2 = ( data['High'] + data['Low'] ) / 2
rsi = calculate_rsi(hl2, period=10)
smoothed_rsi = rsi.rolling(window=3).mean()
slow_smoothed_rsi = rsi.rolling(window=10).mean()
data['rsi_sma_fast'] = smoothed_rsi
data['rsi_sma_slow'] = slow_smoothed_rsi
data['rsi'] = rsi
data['rsi_crossover_fast'] = rsi_crossover(smoothed_rsi)
data['rsi_crossover_slow'] = rsi_crossover_with_sma(rsi , slow_smoothed_rsi)
data['STDEV'] = data['Close'].rolling(window=5).std()
data['Upper_Band'] = data['Close'].rolling(window=5).mean() + (data['Close'].rolling(window=5).std() * 2)
data['Lower_Band'] = data['Close'].rolling(window=5).mean() - (data['Close'].rolling(window=5).std() * 2)
data['candle_type'] = candle_type_value
data['slow_harmonic_mean'] = calculate_harmonic_mean(data['Close'], period=9)
data['fast_harmonic_mean'] = calculate_harmonic_mean(data['Close'], period=3)
data['harmonic_mean_high'] = calculate_hm_high(high)
# data['harmonic_mean_high'] = harmonic_mean_high(high)
# print("New Harmonic Mean",calculate_hm_high(high)[:-20])
# print("Old Harmonic Mean",data['harmonic_mean_high'][:-20])
data['harmonic_mean_low'] = calculate_hm_low(low)
# h4 lag by 3 fixed
features_h4[f'stdev_slow_{candles}_T-{day}'] = close_val.rolling(window=18).std()
features_h4[f'stdev_fast_{candles}_T-{day}'] = close_val.rolling(window=9).std()
features_h4[f'slow_harmonic_mean_{candles}_T-{day}'] = calculate_harmonic_mean(close_value , period=27)
features_h4[f'fast_harmonic_mean_{candles}_T-{day}'] = calculate_harmonic_mean(close_value, period=9)
highest_high = features_h4[f'High_h4_{candles}_T-{day}'].rolling(window=9).max()
lowest_low = features_h4[f'Low_h4_{candles}_T-{day}'].rolling(window=9).min()
# Calculate the relative range
features_h4[f'relative_range_h4_{candles}_T-{day}'] = close_value - ( ( highest_high + lowest_low ) / 2)
supertrend, supertrend_status_value = supertrend_indicator(high_value, low_value, close_value, period= 27, multiplier=5.5)
supertrend_crossover = supertrend_status_crossover(supertrend_status_value)
features_h4[f'supertrend_h4_{candles}_T-{day}'] = supertrend
features_h4[f'supertrend_status_h4_{candles}_T-{day}'] = supertrend_status_value
features_h4[f'supertrend_crossover_h4_{candles}_T-{day}'] = supertrend_crossover