Disclaimer: This project is not maintained anymore. Do reach out if you feel you can contribute in any way.
| Discussion | Bugs/Issues | Demo Tutorial | Contribute |
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| Join/Read the Community Discussion | Raise an Issue about a Problem | Get Help about Usage | Contribute With New Features |
🚀 Introducing Fin-Maestro-Kin:
Our new all-in-one finance API, designed to revolutionize financial data analysis and processing.
✔ Seamlessly fetch historical data, analyze market trends, and evaluate sentiment with ease.
⚡ Empowered with FastAPI brilliance, offering lightning-fast performance and scalability.
📦 Publish your own financial applications powered by Fin-Maestro-Kin and witness unparalleled insights into the market.
🔗 View Fin-Maestro-Kin on PyPI
Fin-Maestro is a cutting-edge web application that aims to make it easier for market participants to operate more effectively and intelligently by thoroughly examining various parameters of various financial instruments.
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Manual: To be able to raise the project you must have the following prerequisites:
[x] Vite.js (https://vitejs.dev/guide/)
[x] python 3.10.11 (https://www.python.org/downloads/release/python-31011/)
In the case of the backend, go to the path ~/backend/scr/ and execute the following command:
pip install -r requirements.txtAdditionally you should consider installing the libraries TA_Lib, tensorflow, stable-baselines3 and tensorflow-intel
In the case of the frontend, go to the path ~/frontend/ and execute the following command:
npm installand then execute:npm run dev -
Docker: To be able to raise the project you must have the following prerequisites:
[x] Docker/Docker-compose (https://docs.docker.com/desktop/)
Go to the main path and run the following command:
docker-compose up -d --build
Run QuestDB Image:
docker run -p 9000:9000 -p 9009:9009 -p 8812:8812 -p 9003:9003 questdb/questdb:7.3.3
- Valuation Determiner
- Mock Trader
- Sentiment Analyzer
- Pattern Analyzer
- Indices Health
- SWOT Analyzer
- Fundamental Scans
- Crypto Technical Scans
- Strategy Backtester
- Buy/Sell Signals Generator
The Valuation Determiner considers a variety of the stock's financial factors before calculating its fair value using the book value, yearly sales, annual earnings, and Graham number. It determines if the stock is undervalued, reasonably valued, or overvalued after the computation is complete.
Input:
1. Stock Name
Output:
1. Valuation as per book value (VAP_BV)
2. Valuation as per annual sales (VAP_SALES)
3. Valuation as per earnings (VAP_EARNINGS)
4. Valuation as per Graham number (VAP_GRAHAM)
5. Status: Undervalued / Fairly Valued / Overvalued
For equities from various sectors, a separate procedure is utilised to determine the fair value. The Valuation Determiner module automatically modifies the stock's fair value computation process to produce enhanced dependability and accuracy.
Reason:
Some stocks tend to trade at higher multiples due to it's nature of the business. Example: FMCG Stocks, monopoly/duopoly stocks
Thus, if such stocks are not differentiated, the result obtained would always indicate that stock is overvalued. This may result
in missed chances to add companies to your portfolio at the right time.
Without using real money for trading, market participants can place simulated trades using Mock Trader. Participants in the market might use these trades to test a certain trading strategy or analysis.
Input:
1. Stock Name
2. Transaction type: Buy/Sell
Output:
1. Trading position in the requested stock.
The Sentiment Analyzer module analyses option chain data for indices and securities to produce a sentiment that indicates whether the index or stock is oversold, slightly oversold, slightly overbought, or overbought.
Input:
1. Stock Name
Output:
1. Sentiment for Nifty and Bank Nifty (needs no user input)
2. Sentiment for requested stock (oversold, slightly oversold, slightly overbought, or overbought)
Finding a certain technical chart pattern across all the stocks is done by the Pattern Analyzer module, which then outputs the stock name and a chart showing the pattern's position with a yellow pointer just above it. Also, it can exclude equities whose technical charts show a breakdown, breakout, or consolidation.
Input:
1. Technical pattern name
Output:
1. Candlestick chart of stocks where the pattern is located indicating the target pattern's position with yellow pointer.
2. List of stocks that witnessing consolidation, breakout or breakdown.
This module employs a color-coded system to show the overall valuation status of all NSE indices. It makes use of the hues red, orange, and green. Green means that the valuations are good enough to begin SIPs, while orange and red indicate that the valuations are neutral and expensive respectively.
Output:
1. List of indices that are colored-wrapped
This module examines a stock's technical parameters and fundamentals before generating a SWOT that lists the stock's strengths, weaknesses, opportunities, and threats.
Inputs:
1. Stock name
Output:
1. SWOT for the requested stock.
This module examines the financial data of the stocks and sorts stocks that are attractive according to their Graham number, book value, annual sales, and earnings.
Output:
1. List of stocks attractive as per its book value.
2. List of stocks attractive as per its sales.
3. List of stocks attractive as per its earnings.
4. List of stocks trading below Graham Number.
For traders' convenience, this module analyses the technical charts of the specified cryptocurrency and then generates a plot showing the automatically generated support and resistance levels for that cryptocurrency.
Inputs:
1. Cryptocurrency name
Output:
1. Technical chart with auto generated plotting of support and resistance levels.
For any requested stock, this module now backtests a hardcoded trading strategy and generates a visually appealing report with information on the number of trades, total returns, maximum drawdown, and average return.
Inputs:
1. Stock name
Output:
1. A plot indicating the backtest results for the requested stock.
For any requested stock, this module produces a visually appealing plot with long/short green and red colored markers respectively as signals. These signals can be used to interpet the further direction of the stock.
Inputs:
1. Stock name
2. Number of signals (total number of buy or sell signals that must be plotted)
Note: For best results, keep this value as 10.
Output:
1. A visually appealing plot indicating buy and sell signals for the requested stock.
- Please feel free to suggest improvements, bugs by creating an issue.
- Please follow the Guidelines for Contributing while making a pull request.
- Please check the Upcoming Features for a list of exciting features we plan to implement in the future.
- DO NOT use the results provided by the web app 'solely' to make your trading/investment decisions.
- Always backtest and analyze the stocks manually before you trade.
- Consult your financial advisor before making any trading/investment decisions.
- The authors/contributors and the web app will not be held liable for your losses (if any).
| Name | GitHub Profile | Description | Followers | Stars |
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| Dev Juneja | devfinwiz | Idea Owner, Backend Developer |  |
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| Yash Khadse | yash-dk | Front-end Developer |  |
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