BT4013 Project - ARIMAMAS

Dir Layout

.
├── data                        # compiled data sets in .pkl
│   ├── data_base_train.pkl        
│   └── ...                 
├── prediction_models           # individual prediction models                   
│   ├── rf_model.py
│   ├── csv_for_stacking
│   │   ├── LSTM_predicted.csv
│   │   └── ...  
│   ├── LSTM_saved_models       # saved models & scaling parameters for each ticker
│   └── ...      
├── weights_allocation          # weights allocation strategies for selected futures  
│   └── ...    
├── arimamas.py                 # define myTradingSystem and mySettings
└── README.md

Dependencies

• numpy >= 1.15.0
• tensorflow 1.7.0
• keras 2.2.4
• lgbm 2.2.3
• statsmodels 0.9.0
• scikit-learn 0.20.2
• pandas 0.23.4

Instructions

Simply run arimamas.py

Indicators to use

For linear models (6): [USA_BC, USA_BOT, USA_CCR, USA_CF, USA_CPICM, USA_GPAY]

For nonlinear models (44): [USA_BC, USA_BI, USA_BOT, USA_CCPI, USA_CCR, USA_CF, USA_CFNAI, USA_CINF, USA_CP, USA_CPI, USA_CPIC, USA_CPICM, USA_CU, USA_DUR, USA_DURET, USA_EXPX, USA_EXVOL, USA_FBI, USA_FRET, USA_GBVL, USA_GPAY, USA_HI, USA_IMPX, USA_IMVOL, USA_IP, USA_IPMOM, USA_LEI, USA_LFPR, USA_MP, USA_MPAY, USA_NAHB, USA_NFIB, USA_NFP, USA_NLTTF, USA_NPP, USA_PFED, USA_PPIC, USA_RFMI, USA_RSEA, USA_RSM, USA_RSY, USA_TVS, USA_UNR, USA_WINV]

Futures with less than 1 MAPE (trade these)

[F_AD, F_AE, F_AH, F_AX, F_BO, F_BP, F_C, F_CA, F_CD, F_CF, F_DL, F_DM, F_DT, F_DX, F_EB, F_EC, F_ED, F_F, F_FC, F_FL, F_FM, F_FP, F_FV, F_FY, F_GC, F_GD, F_GS, F_GX, F_JY, F_LU, F_LX, F_MD, F_MP, F_ND, F_PQ, F_RF, F_RP, F_RR, F_RY, F_SF, F_SS, F_SX, F_TU, F_TY, F_UB, F_US, F_UZ, F_VF, F_VT, F_VW, F_XX, F_YM, F_ZQ]

Models

1. LSTM

Settings:

• LOOKBACK = 30 (trained using days from past 30 days)
• Each time make 1-step prediction
• Include all indicators as predictors

Model Architecture:

model = Sequential()
model.add(LSTM(units=20, input_shape=(lookback, n_features),
               return_sequences=True, dropout=0.5))
model.add(LSTM(10, dropout=0.5)) 
model.add(Dense(units=20))
model.add(Dense(units=10))
model.add(Dense(units=dim_out))
model.add(Activation('linear'))

model.compile(loss='mse', optimizer='adam')
hist = model.fit_generator(generator, epochs=80, verbose=2)

Reproduce the model:

• python ./prediction_models/lstm_for_stacking.py

Generate predictions for stacking:

• python ./prediction_models/LSTM_predict_for_stack.py

2. Linear Regression

Settings:

• Using .shift(1) to get 'LAG_OPEN', 'LAG_HIGH', 'LAG_LOW', 'LAG_CLOSE' as independent variables
• data_train_x & data_test_x consists of 'LAG_OPEN', 'LAG_HIGH', 'LAG_LOW', 'LAG_CLOSE'
• data_train_y & data_test_y consists of 'CLOSE'
• Refer to prediction_models/LR_Model_Coefficients.ipynb for the generated coefficients for each model
• Refer to prediction_models/csv_for_stacking/LR_Model_Predictions_(2016-2018).ipynb for the generated predictions for stacking

Model:

lr = LinearRegression()
model = lr.fit(data_train_x, data_train_y)
y_pred = lr.predict(data_test_x)

3. Random Forest

Settings:

• LOOKBACK = 40
• Each time make 1-step prediction
• Include 44 indicators + lagged prices + moving average prices as predictors
• Final models are retrained on 2010-2018 data

Model Hyperparameters:

• max_depth: 8 - 20
• max_features: 0.5 - 0.9
• min_samples_leaf: 2 - 6
• n_estimators: 80 - 200

Training and generate predictions for stacking:

• python ./prediction_models/rf_for_stacking.py

4. SARIMA

Settings:

• Lookback = 120
• Uses CLOSE prices only
• Refit a model to past 120 days data with grid-searched order/seasonal-order and make 1-step forecast
• Stationarity and Invertibility not enforced in model to prevent raising errors

Model parameters:

• order = (0-2, 1, 1-2)
• seasonal-order = (0-1, 0-1, 1, 20)