Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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Showing 24 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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World class QA, 100% done-for-youMuukTest is a test automation service that combines our own proprietary, AI-powered software with expert QA services to help you achieve world class test automation at a fraction of the in-house costs.
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1
GoCryptoTrader
Trading bot and framework supporting multiple exchanges
GoCryptoTrader is a full framework / bot for cryptocurrency trading, written in Go (Golang). It supports multiple exchanges, real-time and historic data, backtesting, handling order books, portfolio management, scripting, and many exchange integration features. It is a trading engine that can be run by users to automate strategies across many exchanges. Licensed under MIT. Support for all exchange fiat and digital currencies, with the ability to individually toggle them on/off. Customisation of HTTP client features including setting a proxy, user agent and adjusting transport settings. ... -
2
NautilusTrader
A high-performance algorithmic trading platform
NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, provides quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes. The platform is 'AI-first', designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the parity challenge of keeping the Python research/backtest environment, consistent with the production live trading environment. ... -
3
PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance. -
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Awesome-Quant
A curated list of insanely awesome libraries, packages and resources
awesome-quant is a curated list (“awesome list”) of libraries, packages, articles, and resources for quantitative finance (“quants”). It includes tools, frameworks, research papers, blogs, datasets, etc. It aims to help people working in algorithmic trading, quant investing, financial engineering, etc., find useful open source or educational resources. Licensed under typical “awesome” list standards. -
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5
Flowsurface
A native desktop charting platform for crypto markets
...The platform connects directly to public exchange APIs and WebSocket streams from venues such as Binance, Bybit, and OKX, allowing low-latency real-time data ingestion without relying on third-party servers. Users can customize layouts across multiple panes, adjust aggregation intervals, and tailor the visual presentation to suit different trading strategies. -
6
Optopsy
A nimble options backtesting library for Python
...The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simple and easy to implement into your regular Panda's data analysis workflow. As such, we just need to call the long_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. ... -
7
Qbot
AI-powered Quantitative Investment Research Platform
Qbot is an open source quantitative research and trading platform that provides a full pipeline from data ingestion and strategy development to backtesting, simulation, and (optionally) live trading. It bundles a lightweight GUI client (built with wxPython) and a modular backend so researchers can iterate on strategies, run batch backtests, and validate ideas in a near-real simulated environment that models latency and slippage. The project places special emphasis on AI-driven strategies — including supervised learning, reinforcement learning and multi-factor models — and offers a “model zoo” and example strategies to help users get started. ... -
8
Superalgos
Free, open-source crypto trading bot, automated bitcoin trading
...Once you install and launch the app, a series of interactive tutorials take you by the hand and walk you all around the system while you learn the basic skills required to use the interface, mine data, backtest strategies, and even run a live trading session. -
9
Barter
Open-source Rust framework for building event-driven systems
...Use mock MarketStream or Execution components to enable back-testing on a near-identical trading system as live-trading. Centralised cache-friendly state management system with O(1) constant lookups using indexed data structures. Robust Order management system - use stand-alone or with Barter. Turn on/off algorithmic trading from an external process (eg/ UI, Telegram, etc.) whilst still processing market/account data. -
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10
TradingGoose Studio
Technical analysis + LLM powered trading workflows
...It combines traditional technical analysis with modern AI-driven decision-making by allowing users to build workflows where multiple specialized agents collaborate to interpret market signals and execute actions. The platform supports end-to-end trading pipelines, starting from ingesting real-time market data and applying custom indicators, to generating insights and triggering automated trades or alerts. Users can connect their own data providers and define personalized strategies using programmable indicators, making the system highly flexible for different trading styles. A key aspect of the platform is its visual workspace, where charts, widgets, and workflow blocks can be arranged and customized to create an interactive trading environment. -
11
Kalshi Trading Bot CLI
AI-native CLI for trading Kalshi prediction markets
Kalshi Trading Bot CLI is an AI-driven command-line tool designed to automate trading strategies on Kalshi prediction markets by combining quantitative modeling with real-time market data. It operates by conducting deep research on events, generating independent probability estimates, and comparing those estimates against current market prices to identify trading opportunities. The system incorporates advanced decision-making logic, including Kelly criterion-based position sizing and a structured multi-step risk evaluation process before executing trades. ... -
12
Roboquant
User-friendly and completely free algorithmic trading platform
...So whether you are a beginning retail trader or an established trading firm, Roboquant can help you to quickly develop robust and fully automated trading strategies. But perhaps most important of all, it is blazingly fast. Roboquant is orders of magnitude faster than most other algo-trading platforms. With historic data sets becoming more widely available and growing in size, it is important that a strategy can still be quickly developed, back-tested and optimized. If this cycle takes too long, it is nearly impossible to create high-performing and robust strategies. A lot of effort and attention went into making sure Roboquant is easy to use, especially for less experienced developers. -
13
AutoTrader
A Python-based development platform for automated trading systems
AutoTrader is a Python-based platform—now archived—designed to facilitate the full lifecycle of automated trading systems. It provides tools for backtesting, strategy optimization, visualization, and live trading integration. A feature-rich trading simulator, supporting backtesting and paper trading. The 'virtual broker' allows you to test your strategies in a risk-free, simulated environment before going live. Capable of simulating multiple order types, stop-losse,s and take-profits,... -
14
Kalshi-Quant-TeleBot
Kalshi Advanced Quantitative Trading Bot is an enterprise-grade
...Built with cutting-edge quantitative algorithms and professional risk management, it provides institutional-quality trading capabilities with user-friendly control The Kalshi Advanced Quantitative Trading Bot is a professional-grade automated trading system designed specifically for event-based markets on the Kalshi platform. This bot leverages advanced quantitative strategies, machine learning techniques, and real-time data analysis to identify profitable trading opportunities while maintaining robust risk management protocols. Built with a modular architecture, the system combines Python-based trading algorithms with a JavaScript Telegram bot interface for dynamic monitoring and interaction. The bot is designed to operate continuously, making data-driven decisions based on news sentiment analysis, statistical arbitrage opportunities -
15
roboquant
roboquant is a very fast algo-trading platform
Roboquant is an open source algorithmic trading platform written in Kotlin. It is very fast, flexible, user-friendly and completely free to use. It is designed for anyone serious about algo-trading. So whether you are a beginning retail trader or an established trading firm, roboquant can help you to quickly develop fully automated trading strategies. No false promises of making lots of profit without doing the hard work, just a great foundation for building your own strategies. -
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AnyTrading
The most simple, flexible, and comprehensive OpenAI Gym trading
gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex. -
17
TradingGym
Trading backtesting environment for training reinforcement learning
TradingGym is a toolkit (in Python) for creating trading and backtesting environments, especially for reinforcement learning agents, but also for simpler rule-based algorithms. It follows a design inspired by OpenAI Gym, offering various environments, data formats (tick data and OHLC), and tools to simulate trading with costs, position limits, observation windows etc. Licensed under MIT. This training environment was originally designed for tickdata, but also supports OHLC data format. WIP. The list contains the feature columns to use in the trading status. -
18
MarketStore
DataFrame server for financial timeseries data
...If you are struggling with managing lots of HDF5 files, this is perfect solution to your problem. The batteries are included with the basic install, you can start pulling crypto price data from GDAX and writing it to the db with a simple plugin configuration. MarketStore enables you to query DataFrame content over the network at as low latency as your local HDF5 files from disk. -
19
Zipline
Zipline, a Pythonic algorithmic trading library
...Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python package. For a development installation (used to develop Zipline itself), create and activate a virtualenv, then run the etc/dev-install script. Please note that Zipline is not a community-led project. Zipline is maintained by the Quantopian engineering team, and we are quite small and often busy. -
20
AlphaPy
Python AutoML for Trading Systems and Sports Betting
AlphaPy is a Python-based AutoML framework tailored for trading systems and sports betting applications. Built on popular libraries like scikit-learn and pandas, it enables data scientists and speculators to craft predictive models, ensemble strategies, and automated forecasting systems with minimal setup. Run machine learning models using scikit-learn, Keras, xgboost, LightGBM, and CatBoost. Generate blended or stacked ensembles. Create models for analyzing the markets with MarketFlow. Develop trading systems and analyze portfolios using MarketFlow and Quantopian's pyfolio. -
21
EliteQuant
A list of online resources for quantitative modeling, trading, etc.
EliteQuant is a curated directory of online resources for quantitative finance: trading, portfolio management, quantitative modeling, data sources, libraries, platforms, and communities. It is not a software library per se, but a “list of things” - i.e., an aggregator of open source projects, blogs, tools etc., intended to help practitioners find useful resources. It is licensed under Apache-2.0, and maintained by volunteers. A list of online resources for quantitative modeling, trading, and portfolio management. ... -
22
Gekko-Strategies
Strategies to Gekko trading bot with backtests results
Gekko-Strategies is a community repository of strategies (JavaScript files plus configuration) for the Gekko trading bot. It contains a variety of trading strategy scripts, backtest results, and tools or helpers for strategy evaluation. It is not itself a standalone trading engine but contains strategy modules to use with Gekko. Results are sorted by amount of best profit/day on unique DATASETS. Includes an install script (install.sh) to facilitate installing strategies into the user’s Gekko setup under Unix-like systems. ... -
23
LEAN
Lean algorithmic trading engine by QuantConnect
...Use combinations of margin, fill, and slippage models to simulate a liquidity endpoint. 100+ popular technical indicators built, tested, and ready for use. Applicable to any data source. -
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QuantComponents
Free Java components for Quantitative Finance and Algorithmic Trading
... * Highly modular: usable as plain java API, OSGi components, or integrated into Eclipse * Standalone or client-server architecture, depending on performance and reliability needs * Integrated with Interactive Brokers through IB Java API * Generic broker API, it can easily be extended to work with other brokers * It works with historical and/or realtime market data * Backtesting facility * Extensible SWT charting library
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