Merge branch 'master' of https://github.com/drew2323/v2trading

README.md

@@ -1,29 +1,24 @@
-# V2TRADING - Advanced Algorithmic Trading Platform
+# V2TRADING - Algorithmic Trading Platform with Frontend
 
 ## Overview
 Custom-built algorithmic trading platform for research, backtesting and live trading. Trading engine capable of processing tick data, providing custom aggregation, managing trades, and supporting backtesting in a highly accurate and efficient manner.
 
 ## Key Features
-- **Trading Engine**: At the core of the platform is a trading engine that processes tick data in real time. This engine is responsible for aggregating data and managing the execution of trades, ensuring precision and speed in trade placement and execution.
+- **Trading Engine**: Processes tick data in real time, aggregating data and managing trade execution.
 
-- **High-Fidelity Backtesting Environment**: ability to backtest strategies with 1:1 precision - meaning a tick-by-tick backtesting. This level of precision in backtesting, down to millisecond accuracy, mirrors live trading environments and is vital for developing and testing high-frequency trading strategies.
+- **Backtesting**: tick-by tick backtesting, down to millisecond accuracy, mirrors live trading environments and is vital for developing and testing high(er)-frequency trading strategies.
 
-- **Custom Data Aggregation:** The platform includes a data aggregator that allows for custom aggregation rules. This flexibility supports a variety of data analysis approaches, including non-time based bars and other unique criteria.
+- **Configuration**: robust configuration via TOML
+
+- **Frontend**: Frontend to support research to backtesting to paper trading workflow, including lightweight charts.
+
+- **Custom Data Aggregation:** Custom time based, volume based, dollar based and renko bars aggregators based on tick-by-tick data.
 
 - **Indicators** Contains inbuild [tulipy](https://tulipindicators.org/list) [ta-lib](https://ta-lib.github.io/ta-lib-python/) and templates for custom build multioutputs stateful indicators.
 
-- **Machine Learning Integration:** Recently, the platform has expanded to incorporate machine learning capabilities. This includes modules for both training and inference, supporting the complete ML lifecycle. These ML models can be utilized within trading strategies for classification and exploiting statistical advantages.
+- **Machine Learning Integration:** Includes modules for both training and inference, supporting the complete ML lifecycle. 
 
-**Technology Stack**
-**Backend and API:** The backbone of the platform is built with Python, utilizing libraries such as FastAPI, NumPy, Keras, and JAX, ensuring high performance and scalability.
-**Frontend:** The client-side is developed with Vanilla JavaScript and jQuery, employing LightweightCharts for charting purposes. Additional modules enhance the platform's functionality. The frontend is slated for a future refactoring to modern frameworks like Vue.js and Vuetify for a more robust user interface.
-
-While the platform is fully functional and growing, ongoing development is planned, particularly in the realm of frontend enhancements and further integration of advanced machine learning techniques.
-
-**Contributions**
-Contributions to this project are welcome. Whether it's improving the frontend, enhancing the backend capabilities, or experimenting with new trading strategies and machine learning models, your input can help take this platform to the next level.
-
-This repository represents a sophisticated and evolving tool for algorithmic traders, offering precision, speed, and a level of customization that is unparalleled in open-source systems. Join us in shaping the future of algorithmic trading.
+**Gui examples**
 
 <p align="center">
   Main screen with entry/exit points and stoploss lines<br>
@@ -50,6 +45,10 @@ This repository represents a sophisticated and evolving tool for algorithmic tra
   <img width="700" alt="Strategy analytical tools" src="https://github.com/drew2323/v2trading/assets/28433232/4bf8b3c3-e430-4250-831a-e5876bb6b743">
 </p>
 
+**Backend and API:** The backbone of the platform is built with Python, utilizing libraries such as FastAPI, NumPy, Keras, and JAX, ensuring high performance and scalability.
+**Frontend:** The client-side is developed with Vanilla JavaScript and jQuery, employing LightweightCharts for charting purposes. Additional modules enhance the platform's functionality. The frontend is slated for a future refactoring to modern frameworks like Vue.js and Vuetify for a more robust user interface.
+
+**Documentation** Public docs in in progress. Some can be found on [knowledge base](trading.mujdenik.eu) but first please request access. Some analysis documents can be found on [shared google doc folder](https://drive.google.com/drive/folders/1WmYG8oDGXO-lVTLVs9knAmMTmQL4dZt6?usp=drive_link).
 
 # Installation Instructions
 This document outlines the steps for installing and setting up the necessary environment for the application. These instructions are applicable for both Windows and Linux operating systems. Please follow the steps carefully to ensure a smooth setup.

v2realbot/config.py

@@ -11,6 +11,7 @@ _ml_module_loaded = False
 
 #directory for generated images and basic reports
 MEDIA_DIRECTORY = Path(__file__).parent.parent.parent / "media"
+VBT_DOC_DIRECTORY = Path(__file__).parent.parent.parent / "vbt-doc" #directory for vbt doc
 RUNNER_DETAIL_DIRECTORY = Path(__file__).parent.parent.parent / "runner_detail"
 
 #location of strat.log - it is used to fetch by gui

v2realbot/main.py

@@ -1,7 +1,7 @@
 import os,sys
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 os.environ["KERAS_BACKEND"] = "jax"
-from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY, LOG_PATH, MODEL_DIR
+from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY, LOG_PATH, MODEL_DIR, VBT_DOC_DIRECTORY
 from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
 from datetime import datetime
 from rich import print
@@ -122,6 +122,22 @@ async def static_files(request: Request, path: str, authenticated: bool = Depend
 
     return FileResponse(file_path)
 
+@app.get("/vbt-doc/{file_path:path}")
+async def serve_protected_docs(file_path: str, credentials: HTTPBasicCredentials = Depends(authenticate_user)):
+    file_location = VBT_DOC_DIRECTORY / file_path
+
+    if file_location.is_dir():  # If it's a directory, serve index.html
+        index_file = file_location / "index.html"
+        if index_file.exists():
+            return FileResponse(index_file)
+        else:
+            raise HTTPException(status_code=404, detail="Index file not found")
+    elif file_location.exists():
+        return FileResponse(file_location)
+    else:
+        raise HTTPException(status_code=404, detail="File not found")
+
+
 def get_current_username(
     credentials: Annotated[HTTPBasicCredentials, Depends(security)]
 ):