"AI-Trader: 100% Fully-Automated Trading Powered by Agent Swarm Intelligence"

Fully automated crypto trading powered by a custom price prediction AI and a structured/tiered DCA system. Website at PowerTrader.net

⚡ Open nof1.ai | Autonomous AI Trading Agent (自主AI交易系统) | https://open-nof1.195440.com

Backtrader-powered backtesting framework for algorithmic trading, featuring 20+ strategies, multi-market support, CLI tools, and an integrated MCP server for professional traders.

powerful ai-copilot for quant traders and researchers

AI guardian for manual crypto traders — risk monitoring, strategy validation & emotional trading detection. No trade execution.

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Craft complex trading strategies with remarkably simple Python. Access 300+ indicators, multi-symbol/timeframe support, spot/futures trading, partial fills, and risk management tools. Focus on logic, not boilerplate.

Trading signals and cryptocurrency trading tool for Binance Futures

AI-CryptoTrader is a state-of-the-art cryptocurrency trading bot that uses ensemble methods to make trading decisions based on multiple sophisticated algorithms. Built with the latest machine learning and data science techniques, AI-CryptoTrader provides a powerful toolset and advanced trading stratgies for maximizing your cryptocurrency profits.

TradingView webhook bot for manual traders – execute alerts on Binance, Bybit, OKX, and Hyperliquid with risk limits, TP/SL, trailing stop, and optional AI review.

nof0 of AI-Trader，A 股，港股，美股自动交易，并实时跟踪

Bitcoin / Crypto AI Trading Bot

A quickstart for the trader agent for AI prediction markets on Gnosis

An experimental cryptocurrency trading system that combines AI-powered analysis with real-time market data and social sentiment monitoring. Features multiple microservices including market monitoring, social sentiment analysis, AI-driven trading signals, and automated trade execution. Built for fun.

AvantisFi Quantum Trader 2025 - AI-Powered DeFi Yield Optimizer

Python application to show AI functionality based on Keras and TensorFlow

🤖 An intelligent stock trading bot combining technical & qualitative analysis with Interactive Brokers TWS API integration

AI-powered trading bot for Kalshi. Connects GPT-4o or local Ollama to your Kalshi account, feeds news headlines and market context into the model, and places trades automatically when AI probability estimates diverge from live Kalshi prices.

The ultimate Polymarket CopyTrading Bot in Rust. AI-powered Polymarket CopyTrading Bot for smarter trades. Self-hosted Polymarket CopyTrading Bot with dashboard. Real-time Polymarket CopyTrading Bot for top traders. Reliable Polymarket CopyTrading Bot for prediction markets. Fast Polymarket CopyTrading Bot. Try Polymarket CopyTrading Bot now.

A professional-grade, modular, and scalable AI trading bot built for MetaTrader 5. Designed to trade Gold (XAU/USD) using real-time ML trend analysis, NLP-based sentiment interpretation, dynamic risk modeling, and institutional-level strategy execution.

Created an DQN AI Agent which chooses to sell, buy, or keep stocks from various companies.

Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

数字货币-程序化24*365小时自动策略交易PC客户端

NewzTrader - Autonomous Trading Agent Implementation Using Natural Language Processing Of WSJ Headlines

ML Financial API

The ultimate community for Betfair enthusiasts, traders, and tech-savvy bettors! Whether you’re new to the Betfair Exchange or a seasoned trader, this is your hub to explore, share, and master betting and trading strategies powered by machine learning and AI.

这是一个为币安合约设计的AI混合型量化交易机器人，结合了大型语言模型（如 GPT-4）的市场分析与传统技术分析策略（趋势、突破、均值回归）。它具有AI性能反馈机制、高级风险管理（动态仓位、追踪止损）、实时Web监控 和高保真模拟交易模式，旨在实现全天候、自适应的自动化交易。

A repository to scrape profitable wallets from DEX Screener, then analyze their performance with dexcheck.ai. The aim is find profitable traders on the Solana ecosystem so we can copy-trade them using the Trojan bot on Telegram

📈 Base on AI(RL/LSTM) algorithm to trade stock or features