Stock and ETF Database
Building a stock and etf database with postgreSQL and Timescale DB. Gathering data from Alpaca API and IEX Cloud.
Executing differenct SQL quries to answer different questions, such as which stock has been sold or bought by a specific ETF on selected date or current date.
The database also includes comments from wallstreetbets using PushshiftAPI. Questions, such as most mentioned stock on selected time period, can be answered.
Credit: Parttimelarry
Overview
| Language | Python, SQL |
| Database | PostgreSQL, TimescaleDB |
| Toolkits/Library | TablePlus, alpaca_trade_api, psycopg2, aiohttp, asyncpg, asyncio, datetime, time, json, csv |
Stock and Financial Dashboard
Building a financial dashboard using streamlit and redis. Gathering data from IEX Cloud API.
The dashboard consists of total 5 parts.
Showing latest comments from twitter, wallstreetbets and stockwits for the input stock.
The number of most mentioned stock on wallstreetbets is formated and shown on the dashboard.
The dashboard also consists of latest news and charts of the input stock.
This dashboard aims to provide social data overview towards input stock to help user conduct trade execution.
Credit: Parttimelarry
Overview
| Language | Python, SQL |
| Database | PostgreSQL, Redis |
| Toolkits/Library | streamlit, pandas, numpy, requests, tweepy, psycopg2, plotly, datetime, IEX Cloud API |
Value Investing and Momentum Investing Analysis
This project investing strategy that selects the 50 stocks with the best value metrics,
including Price-to-earnings ratio, Price-to-book ratio, Price-to-sales ratio, etc.
Both Value Investing and Momentum Investing are used on the stock evaluation.
The portfolio and trading strategy is output to be an excel file with the help of xlsxwriter.
Credit: freeCodeCamp.org
Overview
| Language | Python |
| Toolkits/Library | Pandas, numpy, requests, xlsxwriter, scipy.stats |
Crypto Trading Bot
This project aims to bulit a crypto trading bot. Supertrend indicators are chosen for the trade execution strategy.
The bot is connected to Binance API and execute trades when criteria are matched. The ccxt Library provides a bridge between crtpyo trading platforms and python.
The data gathered from binance api with ccxt and processed with pandas. The schedule library helps to update the lastest candlestick data every 20s.
The backtesting system also included in the trading bot file. The backtesting system is built under backtesting.py. The data is downloaded directly from cryptodatadownload.com
Credit: Parttimelarry
Overview
| Language | Python |
| Toolkits/Library | Pandas, ccxt, schedule, binance API |
Get In Touch
Should you need more information, feel free to reach me via email at victorwancf@gmail.com. Thank you.