Microservices-Based-Algorithmic-Trading-System
MBATS is a docker based platform for developing, testing and deploying Algorthmic Trading strategies with a focus on Machine Learning based algorithms.
파일 탐색기
최종 버전 다운로드 (.zip)- Dockerfile
- requirements.txt
- Dockerfile
- README.md
- requirements.txt
- Dockerfile
- Dockerfile
- requirements.txt
- Dockerfile
- architecture-cloud.png
- architecture.png
- backtrader.png
- components.png
- logo.PNG
- logo2.png
- logo_0.PNG
- old.png
- superset2.PNG
- cred.cpython-37.pyc
- dag_dynamic.cpython-37.pyc
- dag_fx_data.cpython-37.pyc
- dag_fx_data_download copy.cpython-37.pyc
- dag_fx_data_download.cpython-37.pyc
- dag_helper_files.cpython-37.pyc
- dag_mlflowserve.cpython-37.pyc
- dag_strategy_dynamic.cpython-37.pyc
- example_dag - 2.cpython-37.pyc
- example_dag.cpython-36.pyc
- example_dag.cpython-37.pyc
- fx_data.cpython-37.pyc
- fx_oanda_daily.cpython-37.pyc
- fx_oanda_download.cpython-37.pyc
- fx_oanda_minute.cpython-37.pyc
- mlflow_test.cpython-37.pyc
- test_dag - Copy.cpython-37.pyc
- test_dag.cpython-37.pyc
- dag_fx_data_download.py
- dag_strategy_dynamic.py
- process_configuration.json
- interested_tickers.xlsx
- strategy.csv
- conda.yaml
- MLmodel
- model.pkl
- 1_ml_log.csv
- 2_ml_log.csv
- processed_2_ml_log.csv
- 10-19-2019-checkpoint.ipynb
- Example-checkpoint.ipynb
- Mlflow Serving Test - Only look at this-checkpoint.ipynb
- PODA PATTI-checkpoint.ipynb
- launch.json
- settings.json
- conda.yaml
- ml_log_processed.csv
- MLproject
- train.py
- Example.ipynb
- 124afc37-5bde-4a12-a5fb-c25c49bc48c5
- 3e137754-12cf-4207-ac2d-e5523bb2e476
- 72632a26-21ef-4487-8f34-8201b729213c
- e4af4537-fc0b-4bb0-8ef0-430f8678aaef
- f618e8d8-213d-4139-a7cf-cff78baec139
- pgadmin4.db
- 01_create_user.sh
- __init__.cpython-37.pyc
- __init__.cpython-36.pyc
- __init__.cpython-37.pyc
- __init__.cpython-36.pyc
- __init__.cpython-37.pyc
- oandav20broker.cpython-36.pyc
- oandav20broker.cpython-37.pyc
- __init__.py
- oandav20broker.py
- __init__.cpython-36.pyc
- __init__.cpython-37.pyc
- oandav20feed.cpython-36.pyc
- oandav20feed.cpython-37.pyc
- __init__.py
- oandav20feed.py
- __init__.cpython-36.pyc
- __init__.cpython-37.pyc
- oandav20sizer.cpython-36.pyc
- oandav20sizer.cpython-37.pyc
- __init__.py
- oandav20sizer.py
- __init__.cpython-36.pyc
- __init__.cpython-37.pyc
- oandav20store.cpython-36.pyc
- oandav20store.cpython-37.pyc
- __init__.py
- oandav20store.py
- __init__.py
- fx_oanda_daily.cpython-37.pyc
- fx_oanda_minute.cpython-37.pyc
- fx_oanda_daily.py
- fx_oanda_minute.py
- risk_db_schema_builder.py
- secmaster_db_schema_builder.py
- secmaster_db_symbol_loader.py
- ml_preprocessing.cpython-37.pyc
- ml_preprocessing.py
- bt_analyzers.cpython-37.pyc
- bt_logger_analyzer.cpython-37.pyc
- bt_perform_analyzer.cpython-37.pyc
- bt_pos_perform_analyzer.cpython-37.pyc
- bt_strat_perform_analyzer.cpython-37.pyc
- bt_strategy_id_analyzer.cpython-37.pyc
- bt_transaction_analyzer.cpython-37.pyc
- bt_logger_analyzer.py
- bt_pos_perform_analyzer.py
- bt_strat_perform_analyzer.py
- bt_strategy_id_analyzer.py
- bt_transaction_analyzer.py
- db_cred.cpython-37.pyc
- db_risk_cred.cpython-37.pyc
- db_secmaster_cred.cpython-37.pyc
- oanda_cred.cpython-37.pyc
- db_risk_cred.py
- db_secmaster_cred.py
- oanda_cred.py
- bt_datafeed_postgres.cpython-37.pyc
- bt_datafeed_postgres.py
- run_BT.py
- __init__.cpython-37.pyc
- simple_strategy.cpython-37.pyc
- simple_strategy_2.cpython-37.pyc
- __init__.py
- simple_strategy.py
- simple_strategy_2.py
- args_parse_dict.cpython-37.pyc
- args_parse_other.cpython-37.pyc
- write_to_db.cpython-37.pyc
- args_parse_other.py
- write_to_db.py
- __init__.py
- superset.db
- superset_config.py
- .devcontainer.json
- .env
- .gitignore
- docker-compose.yml
- LICENSE
- README.md
- starter_script.bat
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/saeed349/Microservices-Based-Algorithmic-Trading-System
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd Microservices-Based-Algorithmic-Trading-System
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. Docker
쉬움 추천사전 준비물
- Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
- Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.
3. Run docker compose:
compose 설정 파일에 정의된 서비스들을 대상으로 명령을 실행합니다.
MBATS is a collection of 9 docker containers acting synchronously to create an environment to develop and productionise trading strategies with ease. The main parts of MBATS are as follows.
이 명령어를 터미널에 그대로 입력해 실행하세요.
터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
3. Python
쉬움사전 준비물
8. Go to Jupyter Notebook (http://localhost:8888) and use the Notebook [*Example.ipynb*](./Storage/notebooks/Example.ipynb) to run through the example strategy implementation where you can
이 명령어를 터미널에 그대로 입력해 실행하세요.
In this project all the ML model can be tracked by the MLflow Tracker and the model artifacts are stored in Minio, the main reason for doing so is that later on I can swap Minio for a Cloud object store like S3. The ML models are then served using MLflow pyfunc. We also have the option to serve the model as Rest API using MLflow (code in sample jupyter notebook)
이 명령어를 터미널에 그대로 입력해 실행하세요.
* Jupyter Notebook:http://localhost:8888
이 명령어를 터미널에 그대로 입력해 실행하세요.
에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
// repository documentation
Was this content helpful?
(0 ratings)