Uses Open AI Gym environment to create autonomous cryptocurrency bot to trade cryptocurrencies.

Related tags

Deep Learning Crypto_Bot
Overview

Crypto_Bot

Uses Open AI Gym environment to create autonomous cryptocurrency bot to trade cryptocurrencies.

Steps to get started using the bot:

  1. Sign up for a binance account, you can use this link to create your account: https://accounts.binance.us/en/register?ref=56432230

  2. After Creating an account, go to "API Management" to get your API keys:

       You will receive two API Keys:
   
           API Key
           
           Secret Key-Make sure to save secret key as you will only see this key once.

  3. Download Crypto_Bot_Class.py File from GitHub

         In Crypto_Bot_Class.py file replace "API_KEY" Variable with your API Key from binance
     
     In Crypto_Bot_Class.py file replace "SECRET_KEY" Variable with your Secret Key from binance

  4. Download Model Files from Model Folder on Github In Crypto_Bot_Class.py file replace each:

               XGB_MODEL= **Your XGB_Model File Path**
           
           CROSSNN_MODEL= **Your CrossNN_Model File Path**
           
           MLP_MODEL= **Your MLP_Model File Path**
           
           FOREST_MODEL=**Your Forest_Model File Path**
           
           BIG_NN_MODEL=**Your BIG_NN_Model File Path**

  5. Download Data Files from Data Folder on Github In Crypto_Bot_Class.py file replace each:

             VAL_SET=**Your VALIDATION DATA File Path**
         
         TRAIN_SET**Your TRAIN DATA File Path**

How to select which bots to use:

By Default- VALIDATION=True: This means that your file will run on the Validation data and will print a graph comparing each model to the market over the last three months.

If you change: VALIDATION=False then the each of the 5 Models will be used to make live trades through the binance API. You can select which model you want to make trades by changing the models from True to False:

    Ex: Change: XGB_MODEL_ON=True    to     XGB_MODEL_ON=False
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Comments
  • Linux path?

    Linux path?

    XGB_MODEL=r'C:\Users\wkjon.spyder-py3\XGB_Model.sav' CROSSNN_MODEL= r'C:\Users\wkjon.spyder-py3\Cross_NN_Model.sav' MLP_MODEL= r'C:\Users\wkjon.spyder-py3\MLP_Model.sav' FOREST_MODEL=r'C:\Users\wkjon.spyder-py3\Forest_Model.sav' BIG_NN_MODEL=r'C:\Users\wkjon.spyder-py3\Big_NN\Big_NN_Model.pth'

    Solved

    opened by ceyhanmolla 0
  • AttributeError: 'XGBModel' object has no attribute 'enable_categorical'

    AttributeError: 'XGBModel' object has no attribute 'enable_categorical'

    <bound method Client.get_open_orders of <binance.client.Client object at 0x7f78442fb3d0>> /usr/local/lib/python3.8/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to: https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations warnings.warn( /usr/local/lib/python3.8/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator RandomForestClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to: https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations warnings.warn( /usr/local/lib/python3.8/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator LabelBinarizer from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to: https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations warnings.warn( /usr/local/lib/python3.8/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator MLPClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to: https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations warnings.warn( /usr/local/lib/python3.8/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names warnings.warn( /usr/local/lib/python3.8/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names warnings.warn( /usr/local/lib/python3.8/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names warnings.warn( Traceback (most recent call last): File "Crypto_Bot_Class.py", line 529, in obs_3, reward_3, info_3=env_3.step() File "Crypto_Bot_Class.py", line 276, in step next_state=self.action() File "Crypto_Bot_Class.py", line 315, in action act=self.get_act() File "Crypto_Bot_Class.py", line 364, in get_act predict=self.model.predict_proba(row)[0,1] File "/usr/local/lib/python3.8/dist-packages/xgboost/sklearn.py", line 1348, in predict_proba class_probs = super().predict( File "/usr/local/lib/python3.8/dist-packages/xgboost/sklearn.py", line 879, in predict if self._can_use_inplace_predict(): File "/usr/local/lib/python3.8/dist-packages/xgboost/sklearn.py", line 811, in _can_use_inplace_predict predictor = self.get_params().get("predictor", None) File "/usr/local/lib/python3.8/dist-packages/xgboost/sklearn.py", line 505, in get_params params.update(cp.class.get_params(cp, deep)) File "/usr/local/lib/python3.8/dist-packages/xgboost/sklearn.py", line 502, in get_params params = super().get_params(deep) File "/usr/local/lib/python3.8/dist-packages/sklearn/base.py", line 210, in get_params value = getattr(self, key) AttributeError: 'XGBModel' object has no attribute 'enable_categorical'

    opened by ceyhanmolla 1
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