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feat(Data): added create_target_cum_forward_returns, now using it for FF & LSTM models, with success

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Mark Aron Szulyovszky
2021-11-14 17:25:32 +01:00
parent 478a15a7cb
commit ead7db3a85
2 changed files with 11 additions and 9 deletions
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model_ff.py
+6 -5
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@@ -1,21 +1,22 @@
#%% Import all the stuff, load data, define constants
from load_data import load_files
from load_data import load_files, create_target_cum_forward_returns
import pandas as pd
from tensorflow import keras
from utils.normalize import normalize
import tensorflow as tf
from utils.visualize import visualize_loss
data = load_files('data/', add_features=False, log_returns=False)
data = load_files('data/', add_features=True, log_returns=False)
data.reset_index(drop=True, inplace=True)
data = data[[column for column in data.columns if not column.endswith('volume')]]
# data = data[["ETH_returns", "BTC_returns"]]
data = data[["BTC_returns", "BTC_mom_10", "BTC_mom_20", "BTC_mom_30", "BTC_mom_60", "BTC_vol_10", "BTC_vol_20", "BTC_vol_60", "day_month", "day_week", "month"]]
ticker_to_predict = 'ETH_returns'
ticker_to_predict = 'BTC_returns'
data = create_target_cum_forward_returns(data, ticker_to_predict, 10)
learning_rate = 0.002
batch_size = 64
epochs = 100
epochs = 500
split_fraction = 0.715
train_split = int(split_fraction * int(data.shape[0]))
model_lstm.py
+5 -4
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@@ -1,5 +1,5 @@
#%% Import all the stuff, load data, define constants
from load_data import load_files
from load_data import load_files, create_target_cum_forward_returns
import pandas as pd
from tensorflow import keras
from utils.normalize import normalize
@@ -7,16 +7,17 @@ import tensorflow as tf
from utils.visualize import visualize_loss
from sklearn.preprocessing import StandardScaler
data = load_files('data/', add_features=True, log_returns=True)
data = load_files('data/', add_features=True, log_returns=False)
data.reset_index(drop=True, inplace=True)
data = data[[column for column in data.columns if not column.endswith('volume')]]
data = data[["BTC_returns", "BTC_mom_10", "BTC_mom_20", "BTC_mom_30", "BTC_vol_10", "BTC_mom_20", "BTC_vol_20"]]
data = data[["BTC_returns", "BTC_mom_10", "BTC_mom_20", "BTC_mom_30", "BTC_mom_60", "BTC_vol_10", "BTC_vol_20", "BTC_vol_60", "day_month", "day_week", "month"]]
ticker_to_predict = 'BTC_returns'
data = create_target_cum_forward_returns(data, ticker_to_predict, 10)
learning_rate = 0.002
batch_size = 64
epochs = 100
epochs = 500
split_fraction = 0.715
train_split = int(split_fraction * int(data.shape[0]))