55 skills found · Page 1 of 2
nautechsystems / Nautilus TraderProduction-grade Rust-native trading engine with deterministic event-driven architecture
DaveSkender / Stock.IndicatorsStock Indicators for .NET is a C# NuGet package that transforms raw equity, commodity, forex, or cryptocurrency financial market price quotes into technical indicators and trading insights. You'll need this essential data in the investment tools that you're building for algorithmic trading, technical analysis, machine learning, or visual charting.
0xfdf / ToranikoA multi-factor equity risk model for quantitative trading.
KidQuant / Pairs Trading With PythonThis project involves using a combination of statistics along with financial thoery to demonstrate a popular trading strategy used in equity markets: Pairs Trading.
tradermonty / Claude Trading SkillsClaude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development.
jerryxyx / AlphaTradingAn workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.
FueledByChai / SumZeroTradingDEPRECATED See FueledByChaiTrading Repo: A Java API for Developing Automated Trading Applications for the Equity, Futures, and Currency Markets
enricoschumann / PMwRPortfolio Management with R: Backtesting investment and trading strategies, computing profit-and-loss and returns, reporting, and more.
Yvaine-Zhang / Models For Intraday Trading Volume PredictionHaving effective intraday forecast for the level of trading volume is of vital importance to algorithmic trading and portfolio management since it attempts to minimize transaction costs by optimally scheduling and placing. The purpose of this project is to create dynamic statistical models of intraday trading volume prediction (in Python). By assuming the stable U shape distribution of intraday trading volume, we apply Deterministic blend, Lognormal Bayesian, Kalman filter and ARIMA model to estimate and generate out of sample forecast on 12 US equity sector ETFs. Results show that some of the proposed methods are able to obviously outperform common volume forecasting methods.
linhnguyen215538 / Volatility Study• Conducted a volatility study to develop pairs trading strategy by writing web crawlers that automated extracting 30 equity and ETF spot and options prices data from CBOE and Yahoo Finance • Utilized NumPy, Pandas, and SciPy packages to calculate implied volatility, realized volatility, and risk premiums to measure how the market prices risk • Gathered and plotted daily VIX futures data with Selenium, Seaborn and Matplotlib to study volatility term structure • Examined volatility clustering and built forecasting tools for market risk using correlations of daily absolute returns and volatility at different time lags
ketan1741 / Benjamin Graham And Warren Buffett Model Stock Exchange There are about 4000 stocks which are actively traded on the stock exchanges at BSE and NSE. Can we extract public financial data from sites like moneycontrol.com to find which are the fundamentally strong stocks. On what stocks would the father of value investing, Benjamin Graham and Warren Buffett the most successful investors in the world make their investments on. Benjamin Graham and Warren Buffett Model Step 1: Filter out all companies with sales less than Rs 250 cr. Companies with sales lower than this are very small companies and might not have the business stability and access to finance that is required for a safe investment. This eliminates the basic business risk. Step 2: Filter out all companies with debt to equity greater than 30%. Companies with low leverage are safer. Step 3: Filter out all companies with interest coverage ratio of less than 4. Companies with high interest coverage ratio have a highly reduced bankruptcy risk. Step 4: Filter out all companies with ROE less than 15% since they are earning less than their cost of capital. High ROE companies have a robust business model, which generates increased earnings for the company typically. Step 5: Filter out all companies with PE ratio greater than 25 since they are too expensive even for a high-quality company. This enables us to pick companies which are relatively cheaper as against their actual value. He points out that applying these filters enables us to reduce and even eliminate a lot of fundamental risks while ensuring a robust business model, strong earning potential and a good buying price.
SharmaVidhiHaresh / Backtesting Trading Strategies With PythonIn this project, I had backtested the cross-over trading strategy on Google Stock from Jan 2016 to June 2020. By using historical time-series data, I had tested the Moving Average(MA) cross-over strategy and Relative Strength Index (RSI) strategy with a stop loss at a price that closes 2% or more below 10-day MA. I had plotted the equity curve with drawdowns and P&L, as well as volume, relative strength index (RSI), stock pricing chart and simple moving averages.
jsisaacs / QuantStrategies💸 A long-short equity quantitative trading strategy (sentiment-based)
TheHardeep / FenixA Python library for trading in the Indian Finance Sector with support for 15+ broker APIs.
kriasoft / Market DataKriaSoft Market Data Server - A local database server with quotes and trade-related data associated with equity, fixed-income, financial derivatives, currency, and other investment instruments.
thammo4 / UvatradierPython wrapper for the Tradier brokerage API
zameyer1 / Evolutionary Trading StrategiesThis code illustrates the use of genetic programming to evolve financial trading strategies for a single equity stock. Individuals (strategies) are considered as functions of historical price data, outputting a position allocation. Strategy fitness evaluation is computed by simulating the strategy over historical financial data. Because financial investment requires a fundamental tradeoff between risk and return, strategies are evaluated on multi-objective fitness functions depending on profit and maximum drawdown of the strategy and ranging from very risk-prone to very risk-averse. The population of individual strategies is evolved using tournament selection, single-point crossover, and random mutation as evolutionary operators. Strategies with the best fitness at any stage in the evolutionary process are recorded in a ‘hall-of-fame’. At the end of the evolutionary process, strategies in the ‘hall-of-fame’ are evaluated over a set of test data and selected based on a train-test criterion which penalizes strategies that do not generalize well.
vinay-ram1999 / AlgoTrade APIFully automated NSE Stock/Equity trading-bot for Kotak Securities API with integrated back testing and ML algorithms.
mobeigi / Algorithmic TradingAlgorithmic Trading software designed to make money on the equity market.
suislanchez / Stocks Crypto Paper TraderSimulated real time trading app for stocks and crypto with RAG LLM that can execute orders (GPT4o, Claude 3.5, AI web search w/ Perplexity Sonar) Supports futures, options, automated risk management (stop-loss, DRIP), tax/P&L realism, current news and equity sentiment, portfolio risk and diversification analysis. Modern, simple and educational.
