Trading & Market Analytics

Market data terminals, trading research workbenches and AI agents for financial analysis.

11 results in Trading & Market Analytics

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alphai-tuiTrading & Market Analyticsalphai-tui is an MIT-licensed terminal stock dashboard written in Rust. It shows quotes and candlestick charts from Yahoo, Finnhub, Alpaca or Tiingo next to AlphAI news scores, SEC Form 4 insider filings, earnings reads and a macro calendar.Open sourceSoftwareFreemium

Views · 7 daysTop 29%

1 views92 stars

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AAutoHedgeTrading & Market AnalyticsAutoHedge is an MIT-licensed Python framework of Director, Quant, Risk and Execution agents for market analysis and autonomous trading on Solana.Open sourceSelf-hosted

Views · 7 daysTop 10%

3 views6,637 stars

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Ddaily_stock_analysisTrading & Market Analyticsdaily_stock_analysis is an MIT-licensed app that analyzes watchlists with market data, news and language models and sends scheduled reports by chat or email.Open sourceSelf-hosted

Views · 7 daysTop 29%

1 views67,647 stars

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FTFincept TerminalTrading & Market AnalyticsFincept Terminal is a native desktop app for financial analytics, market data connectors and AI agents. The repository edition is free under AGPL-3.0.Open sourceSelf-hosted

Views · 7 daysNo views in 7 days

0 views33,521 stars

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HummingbotTrading & Market AnalyticsHummingbot is an Apache 2.0 framework for automated crypto trading strategies. It runs locally or in Docker, connects to centralized and decentralized exchanges and offers scripts, controllers, executors, paper trading and the hbot CLI.Open sourceSelf-hosted

Views · 7 daysTop 29%

1 views20,976 stars

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KronosTrading & Market AnalyticsKronos is a family of open-source decoder-only models pre-trained on candlestick data from over 45 global exchanges, with Python inference and fine-tuning.Open sourceSelf-hosted

Views · 7 daysTop 7%

4 views47,055 stars

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OctoBotTrading & Market AnalyticsOctoBot is a GPL-licensed, self-hosted crypto trading bot by Drakkar-Software. It automates DCA, grid, basket, LLM and TradingView strategies on CCXT exchanges, with a simulator, backtesting and a new node mode.Open sourceSelf-hostedFreemium

Views · 7 daysNo views in 7 days

0 views6,906 stars

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OpenBBTrading & Market AnalyticsOpenBB's Open Data Platform is an open-source layer that exposes financial datasets to Python, REST APIs and apps. The project has announced it is winding down.Open sourceSelf-hosted

Views · 7 daysNo views in 7 days

0 views75,163 stars

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TTradingAgentsTrading & Market AnalyticsTradingAgents is an open-source research framework that combines financial, news, sentiment and technical analysis with agent debates, portfolio risk review and historical decision evaluation.Open sourceSelf-hosted

Views · 7 daysNo views in 7 days

0 views121,000 stars

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TSP (tick-stock-panel)Trading & Market AnalyticsTSP is an MIT-licensed, self-hosted workbench for Chinese A-share strategies, factor research, backtesting and monitoring, with core features exposed as an API.Open sourceSelf-hosted

Views · 7 daysTop 5%

5 views7,207 stars

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VVibe-TradingTrading & Market AnalyticsVibe-Trading connects natural-language finance research with backtests, portfolio snapshots and simulated trading. It provides a self-hosted Web interface, CLI, REST API and MCP tools.Open sourceSelf-hosted

Views · 7 daysTop 16%

2 views37,790 stars

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Shortlists for Trading & Market Analytics

Explore collections with tools relevant to this category, grouped by deployment, source model or workload.

How to choose trading & market analytics tools

Trading and market analytics tools gather price feeds, fundamentals and news, then help users analyse them through terminals, notebooks, backtests or AI agents that debate a trade idea. Many recent projects combine language models with quantitative pipelines to screen watchlists or generate research notes. They differ from general BI tools by handling tickers, time series and financial data sources natively, and they support research rather than brokerage or payments.

Read the guide

Who uses these tools

Quantitative developers prototyping and backtesting strategies
Retail investors researching watchlists with automated reports
Data scientists testing forecasting models on price series

Capabilities to compare

Connectors to market, fundamental and news data
Backtesting with realistic fees and slippage
Multi-agent analysis producing structured research notes
Time series forecasting on candlestick data
Notebook, terminal or API access to datasets

Operating considerations

Data vendor licences and redistribution limits
Model API spend on scheduled analysis runs
Market coverage by exchange, region and asset class
Look-ahead bias and survivorship issues in historical data

Selection checklist

List the markets and data frequencies you need, then confirm each tool's sources cover them legally.
Rerun a published backtest yourself and check fees, slippage and data alignment before trusting results.
Separate research output from execution and keep any live trading behind manual review and limits.
Check whether language model outputs cite the data points they rely on so reasoning can be audited.

What belongs in this category: A product belongs here when it is built to retrieve, model or analyse financial market data for research and strategy testing, rather than executing payments, keeping accounts or generic business reporting.