batch_size: 128            # batchsize
bias: true                 # whether to use bias 
hmax: 100                  # scaling actions, which is good for training. Only used in 'multiple' model type now 
buy_cost_pct: 6.87e-05     # may change according to your market
data_path: ./data/SSE50_sample.npz  # your training data path. shape: (batchsize, sequence_length, stock_num, feature_num)
dropout_rate: 0.25         # drpoout rate of the whole network
feature_size: 1470         # feature_num   49 for SSE50, 6 for DOW30, 43 for COIN
gamma: 1                   # gamma
head_mask: 10              # get rid of the fisrt 10 timesteps since the historical data are not sufficient 
hidden_size: 50            # hidden_size of the LSTM
learning_rate: 0.0001      # learning rate of the model
lstm_layers: 3             # layers of the LSTM network
model_path: ./saved_models # model save path
reward_scaling: 1000       # good for training
sell_cost_pct: 0.0010687   # may change according to your market
sequence_length: 140       # the timestep of each bash
stock_num: 30              # stock numbers   30 for SSE50, 30 for DOW30, 3 for COIN
tail_mask: 130             # get rid of the last 10 timesteps since the expectation results may not be credible enough
tb_path: ./tb_results/     # tensorborad log path 
total_money: 100           # inital money
model_save_interval: 5     # interval of saving the model
use_difficulty: True       # Whether to use difficulty (Good for training)
total_steps: 100           # In these steps difficulty are changing gradually from 0 to 1
epochs_per_step: 10        # epochs in each step