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User Guide

Configuration File

Under the PGPortfolio/pgportfolio directory, there is a json file called net_config.json, holding all the configuration of the agent and could be modified outside the program code.

Network Topology

  • "layers"
    • layers list of the CNN, including the output layer
    • "type"
      • domain is {"ConvLayer", "FullyLayer", "DropOut", "MaxPooling", "AveragePooling", "LocalResponseNormalization", "SingleMachineOutput", "LSTMSingleMachine", "RNNSingleMachine"}
    • "filter shape"
      • shape of the filter (kernal) of the Convolution Layer
  • "input"
    • "window_size"
      • number of columns of the input matrix
    • "coin_number"
      • number of rows of the input matrix
    • "feature_number"
      • number of features (just like RGB in computer vision)
      • domain is {1, 2, 3}
      • 1 means the feature is ["close"], last price of each period
      • 2 means the feature is ["close", "volume"]
      • 3 means the features are ["close", "high", "low"]

Market Data

  • "input "
    • "start_date"
      • start date of the global data matrix
      • format is yyyy/MM/dd
    • "end_date"
      • start date of the global data matrix
      • format is yyyy/MM/dd
      • The performance could varied a lot in different time ranges.
    • "volume_average_days"
      • number of days of volume used to select the coins
    • "test_portion"
      • portion of backtest data, ranging from 0 to 1. The left is training data.
    • "global_period"
      • trading period and period of prices in input window.
      • should be a multiple of 300 (seconds)
    • "coin_number"
      • number of assets to be traded.
      • does not include cash (i.e. btc)
    • "online"
      • if it is not online, the program will select coins and generate inputs from the local database.
      • if it is online, new data that dose not exist in the database would be saved

Training and Tuning the hyper-parameters

  1. First, modify the PGPortfolio/pgportfolio/net_config.json file.
  2. make sure current directory is under /PGPortfolio/ and type python main.py --mode=generate --repeat=1
    • this will make 1 subfolders under the train_package
    • in each subfolder, there is a copy of the net_config.json
    • --repeat=n, n could followed by any positive integers. The random seed of each the subfolder is from 0 to n-1 sequentially.
      • Notably, random seed could also affect the performance in a large scale.
  3. type python main.py --mode=train --processes=1
    • this will start training one by one of the n folder created just now
    • do not start more than 1 processes if you want to download data online
    • "--processes=n" means start n processes running parallely.
    • add "--device=gpu" if your tensorflow support gpu.
      • On GTX1080Ti you should be able to run 4-5 training process together.
      • On GTX1060 you should be able to run 2-3 training together.
    • Each training process is made up from 2 stages:
      • Pre-training, log example:
INFO:root:average time for data accessing is 0.00070324587822
INFO:root:average time for training is 0.0032548391819
INFO:root:==============================
INFO:root:step 3000
INFO:root:------------------------------
INFO:root:the portfolio value on test set is 2.24213
log_mean is 0.00029086
loss_value is -0.000291
log mean without commission fee is 0.000378

INFO:root:==============================


  * Backtest with rolling train, log example:

        DEBUG:root:==============================
INFO:root:the step is 1433
INFO:root:total assets are 17.732482 BTC
  1. after that, check the result summary of the training in nntrader/train_package/train_summary.csv
  2. tune the hyper-parameters based on the summary, and go to 1 again.

Logging

There are three types of logging of each training.

  • In each subfolder
    • There is a text file called programlog, which is the log generated by the running programming.
    • There is a tensorboard folder saves the data about the training process which could be viewed by tensorboard.
      • type tensorboard --logdir=train_package/1 to use tensorboard
  • The summary infomation of this training, including network configuration, portfolio value on validation set and test set etc., will be saved in the train_summary.csv under train_pakage folder

Save and Restore of the Model

  • The trained weights of the network are saved at train_package/1 named as netfile (including 3 files).

Download Data

  • Type python main.py --mode=download_data you can download data without starting training
  • The program will use the configurations in PGPortfolio/pgportfolio/net_config to select coins and download necessary data to train the network.
  • The downloading speed could be very slow and sometimes even have error in China.
  • For those who cann't download data, please check the first release where there is a Data.db file, put it in the database folder. Make sure the online in input in net_config.json to be false and run the example.
    • Note that using the this file, you shouldn't make any changes to input data configuration(For example start_date, end_date or coin_number) otherwise incorrect result might be presented.

Back-test

Note: Before back-testing, you need to suceessfully finish training of algo first

  • Type python main.py --mode=backtest --algo=1 to execute backtest with rolling train(i.e. online learning in supervised learning) on the target model.
  • --algo could be either the name of traditional method or the index of training folder

Tradition Agent

OLPS summary:

Plotting

  • type python main.py --mode=plot --algos=crp,olmar,1 --labels=crp,olmar,nnagent ,for example, to plot
  • --algos could be the name of the tdagent algorithms or the index of nnagent
  • --labels is the name of related algorithm that will be shown in the legend
  • result is

present backtest results in a table

  • type python main.py --mode=table --algos=1,olmar,ons --labels=nntrader,olmar,ons
  • --algos and --lables are the same as in plotting case
  • result:
           average  max drawdown  negative day  negative periods  negative week  portfolio value  positive periods  postive day  postive week  sharpe ratio
nntrader  1.001311      0.225874           781              1378            114        25.022516              1398         1995          2662      0.074854
olmar     1.000752      0.604886          1339              1451           1217         4.392879              1319         1437          1559      0.035867
ons       1.000231      0.217216          1144              1360            731         1.770931              1416         1632          2045      0.032605
  • use --format arguments to change the format of the table, could be raw html csv or latex. The default one is raw.