Daily Stock Analysis reads your watchlist every trading day and hands back a decision dashboard, with a score, a trend call, entry and exit levels, risk alerts, and catalysts for each stock. Quotes, K-lines, technical indicators, announcements, news, and fundamentals from several data sources feed a report written by the language model you connect, including Gemini, Claude, OpenAI-compatible endpoints, DeepSeek, or a local Ollama model. The web workspace covers manual analysis, task progress, history, full Markdown reports, backtesting, portfolio tracking, settings, and light and dark themes. Strategy chat answers follow-up questions in multiple turns using 15 built-in strategies, from moving-average crossover to Chan theory and wave theory. Smart import reads a screenshot, CSV, Excel file, or clipboard text to build the watchlist, with code, name, pinyin, and alias autocomplete. Reports are delivered to WeChat Work, Feishu, Telegram, Discord, Slack, or email on a schedule that skips market holidays. A market review summarizes index moves, breadth, and leading sectors. ETF rotation backtests compare annual results and flag data quality problems. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

OpenStock delivers a production-ready financial market terminal that unifies real-time equity pricing, technical analysis, and multi-asset watchlists into a personal self-hosted dashboard. Investors can monitor equities across thirty international exchanges, inspect live candlestick charts powered by TradingView, and evaluate technical momentum oscillators without paying recurring platform subscriptions. The global command palette accessible via keyboard shortcuts enables instantaneous ticker discovery across global exchange symbols and economic sectors. Users can organize individual stock watchlists in MongoDB, configure automated price threshold alerts triggered every fifteen seconds, and review cross-platform sentiment scores synthesized from Reddit, X, and financial news feeds. The built-in automation engine schedules weekly digest reports and event-driven notifications dispatched directly to your inbox through Nodemailer transports. Responsive market heatmaps and top-mover widgets surface daily sector performance before the opening bell rings. Deployable via Docker Compose with dedicated MongoDB persistence, the platform scales across multi-key Finnhub rate limits effortlessly. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

From raw market data to a simulated trade, every step of quant research happens in one web console. A home dashboard shows index levels, a paper account, strategy status, and recent fills at a glance, and a market analysis page tracks sector rotation and money flow. A stock terminal pairs candlestick charts with company profiles, financial and valuation tabs, and model score curves. Researchers describe a trading hypothesis in plain language, and the factor mining workspace drafts expressions, evolves them, and stores the winners in a factor library. The training studio offers thirteen algorithms, from boosted trees to recurrent networks, with walk-forward stability checks and local or remote GPU scheduling. The backtest center reports returns, Sharpe ratio, drawdown, and an equity curve for any date range, stock pool, and rebalance rhythm. Daily batch inference ranks the whole market, and a rolling news feed tags headlines as bullish or bearish. Paper trading replays real exchange rules, including price limits, lot sizes, fees, and slippage. A chat assistant can run these steps from a sentence, including through messaging apps. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

OpenBB stands as the world's most popular open-source finance repository — an Open Data Platform that evolved from a pandemic-era Reddit post into a full-blown infrastructure layer challenging Bloomberg's $25 billion empire. The "connect once, consume everywhere" architecture consolidates proprietary, licensed, and public financial data sources into multiple consumption surfaces simultaneously: Python environments for quants building models, OpenBB Workspace and Excel for analysts creating dashboards, MCP servers for AI agents performing automated research, and REST APIs for custom applications. The modular extension system supports dozens of data providers including Yahoo Finance, Alpha Vantage, FRED, Intrinio, Polygon, and Tradier with standardized schemas that normalize responses across vendors. The CLI offers a terminal-style interactive interface with autocomplete, parameter hints, and chart rendering for rapid data exploration. Provider routing handles authentication, rate limiting, and response normalization transparently so switching between free and premium data sources requires changing a single parameter. The platform covers equities, options, fixed income, crypto, forex, ETFs, mutual funds, economics, technical analysis, quantitative analysis, and alternative data across global markets. AI agent integration exposes every data endpoint as tool-callable functions with schema discovery enabling LLMs to query financial data programmatically. Install via pip with Python 3.9+ and deploy the REST API server for team access. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Built atop the TauricResearch TradingAgents framework with nearly 100,000 GitHub stars, TradingAgents GUI transforms a CLI-only multi-agent LLM stock analysis pipeline into a polished web application accessible at localhost:5000. The system deploys twelve specialized AI agents — fundamental analysts, sentiment experts, technical analysts, bull and bear researchers, a trader, risk management team, and portfolio manager — who collaboratively debate market conditions through structured LangGraph workflows before producing a final BUY, SELL, or HOLD recommendation. The interface supports ten LLM providers including OpenAI, Anthropic, Google, OpenRouter, DeepSeek, Ollama, xAI, Qwen, GLM, and MiniMax, with a first-run wizard that auto-detects configured API keys and tests connections. A live pipeline visualization shows each agent's status with real-time progress bars, while the tabbed output area separates Live Feed, Reports preview, and Tool calls into dedicated panes. The three-pane Reports tab provides searchable indexing, table-of-contents navigation, and export to Markdown, HTML, or PDF formats. Report length control across Concise, Standard, and Comprehensive modes saves up to 50% on token costs. Multi-session chat allows pinning past reports as grounding context with live token counting and context-window warnings. Three built-in themes — Terminal, Modern, and Bloomberg — persist per browser. Docker Compose deployment maps port 5000 with persistent report storage. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Screen equities against dozens of technical indicators, backtest algorithmic trading strategies, and spot intraday market anomalies using Tick Stock Panel, a self-hosted quantitative analysis workbench. Traders can filter entire exchanges across technical indicators including moving averages, relative strength index, and Bollinger bands with near-instantaneous execution. The platform provides a full backtesting environment where users test customized entry and exit rules, account for simulated slippage and commission fees, and review streamed performance metrics. Dedicated monitoring modules track intraday volume spikes, price breakouts, and multi-day limit streaks with automated alert dispatching to messaging channels and voice announcements. Users can explore individual stock profiles featuring multi-dimensional technical breakdowns, financial balance sheets, and automated event post-mortems generated via integrated language models. Visual factor mining tools allow analysts to discover uncorrelated signals and assemble composite portfolio screens without external database overhead. An interactive dashboard aggregates sector rotations, hot concept heatmaps, and customizable watchlists into modular operational views. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

PanWatch's three scheduled AI agents plan the trading day before the bell, watch the session, and review it after the close for your China A-share, Hong Kong, and U.S. holdings. Day one begins with the portfolio: several brokerage accounts, holdings and profit and loss, and search for stocks or listed ETFs by code or name. The pre-market agent folds overnight moves, news, and technical structure into that plan and skips exchange holidays. Through the session, the intraday monitor watches for unusual moves and technical signals. Price alerts combine price, percentage change, turnover, and volume ratio with AND/OR logic, cooldowns, and expiry times. Each rule picks its own channel from Telegram, WeCom, DingTalk, Feishu, Bark, or a webhook. Spot something odd? The brain icon next to a holding starts TradingAgents, where four analyst roles feed a bull and bear debate, a risk review, and a portfolio-manager decision, with the reasoning trail in the app and your notifications. The opportunities screen ranks AI-scored ideas with entry levels and targets, and paper trading simulates signal-based entries and exits and tracks equity and performance. At day's end, the daily report reviews the session and prepares the next trading day's plan. OpenAI-compatible providers or local Ollama models power the analysis, and the PWA installs on a phone in English or Simplified Chinese. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Stock Screener scans twelve global markets every day so growth investors can find CANSLIM and Minervini style candidates without stitching together spreadsheets and charting sites. You run bulk scans with more than eighty technical and fundamental filters, then rank results by composite scores, relative strength sparklines, and multi-screener ratings that show which names pass several methodologies at once. Market health and exposure views translate breadth into position-sizing guidance, while StockBee style breadth pages track daily movers, advance and decline lines, new highs and lows, and benchmark overlays from one shared calculation layer. Industry group rankings order sectors and groups by relative strength with one-week to six-month movers, and the Relative Rotation Graph plots groups moving through leading, weakening, lagging, and improving quadrants. The stock matrix lays out constituents in grid or cluster layouts colored by change or relative strength, watchlists organize into drag-and-drop folders with sparklines, and theme discovery watches news and social feeds for emerging narratives. An AI research chatbot answers questions against the same daily data, an options command center adds gamma and volatility context for top candidates, and backtesting validates published picks against price history. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

OpenCharts puts a full trading terminal in your browser, with candlestick charts, drawing tools, indicators, and a paper-trading account funded with one hundred thousand dollars. You switch between eight timeframes from one minute to one week, overlay volume, bid and ask lines, and a bar countdown, then add SMA, EMA, RSI, MACD, Bollinger Bands, ATR, Stochastic, or VWAP studies from the toolbar. The drawing rail covers trend lines, rays, channels, Fibonacci retracement and extension, shapes, and text, with magnet snapping to price levels and an object tree that manages every annotation. Position tools calculate risk, reward-to-risk ratio, and position size against live account equity as you drag them across the chart. Market, limit, and stop orders rest in the book, trigger on price ticks, and honor take-profit and stop-loss levels you can drag directly on the chart. Positions mark to market on every tick, and the panels track equity, balance, used margin, and free margin in real time. Session replay rewinds one-minute history and plays it forward at up to ten times speed, so you can rehearse entries against past price action. A trade journal records notes, tags, emotions, and ratings per trade, filtered by symbol when you review the week. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Dealer gamma exposure, max pain, open interest, and the full IV surface for an entire options watchlist land in one self-hosted dashboard that keeps every snapshot it collects. Add any US-listed ticker, press Collect data, and a banner above every view states net GEX, call and put walls, the gamma flip, and a plain-language regime label. The GEX heatmap lays out strike by expiry with historical replay, and the Changes tab compares two trading days side by side. A Contract view splits one option's price move into delta, gamma, vega, theta, and an honest residual. The screener lists every contract with seven greeks and range filters, unusual activity flags volume outliers against each contract's own history, and OI Delta ranks the biggest overnight position changes. Collection can run automatically from every 15 minutes to once a day, and it sleeps through closed markets to save disk space. The 3D volatility surface and skew slices show how implied volatility is priced across the chain. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Most traders know their balance and almost nothing else, because the patterns that actually cost money hide across hundreds of individual fills. Trading Journal AI rebuilds your round-trip trades from a broker CSV, grouping partial fills, scale-ins and shorts automatically, then tracks win rate, profit factor, expectancy, drawdown, MFE, MAE and exit efficiency for every account. Thinkorswim and Interactive Brokers import directly, seven more brokers including Tradovate, NinjaTrader, Webull and tastytrade are in beta, and a generic template covers any other broker's export. The Leak Finder ranks groups of trades that share something you knew at entry, an entry window, a weekday, a size habit, an emotion from your diary, and shows which of them quietly drain the account, with confidence scored against the rest of your history. Exit Lab replays your completed stock trades on one-minute prices and compares your actual exits against 1R and 2R targets, breakeven rules, trailing stops and VWAP or EMA exits, in R and in dollars. If you add an Anthropic key, Claude reads your trading diary and grades each day on process rather than outcome; without one, every number still works, because the statistics are plain code and never AI. Four interface themes, one SQLite file, no account and no telemetry complete the picture. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Broker exports pile up as spreadsheets nobody reads; TradeTally turns them into a trading journal that shows how you actually trade. Import trades from Lightspeed, Charles Schwab, ThinkorSwim, IBKR, E*TRADE, and ProjectX across stocks, options, and futures. Candlestick charts mark each entry and exit, and the trade list adds net and gross P&L, confidence, quality grades, and sectors. Break performance down by hold time, weekday, symbol, strategy, or trade size, and use R-multiple views to judge stop-loss discipline. A color-coded calendar shows daily and weekly results. Every session gets pinned notes, market bias, key levels, watchlists, tags, and templates. Behavioral reports flag revenge trading, loss aversion, and missed exits. Watchlists follow live prices and news, price alerts arrive by email or browser, and earnings dates are listed for the symbols you follow. AI assistants suggest improvements from your own data, achievements and leaderboards reward good habits, and a companion iOS app mirrors the web features. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

A trading journal you host yourself, Trade Journal rebuilds broker fills and statement exports into round-trip trades, a profit and loss calendar, and daily notes that show what actually works. Read-only sync covers more than twenty brokers and exchanges, including Alpaca, Binance, Coinbase, Kraken, and IBKR Flex. Statement import auto-detects TradingView, MetaTrader, NinjaTrader, Tradovate, and thinkorswim files, handles TradeZella and Tradervue CSV migrations, and maps columns for anything else. Trades can be matched first-in-first-out, last-in-first-out, or by weighted average, with partial fills, scale-ins, and futures multipliers handled. The dashboard offers gauges, cumulative and daily P&L, an Edge Score radar, and cards you can rearrange into named layouts, while Reports break results down by symbol, tag, mistake, playbook, weekday, and time of day. Each trade page charts entries and exits and can replay candles you load, with estimated MAE and MFE. Daily journal pages hold Markdown notes, templates, attachments, and voice dictation, and optional AI recaps use your own Anthropic or OpenAI key or a local Ollama model. Playbook checklists score rule adherence, a prop firm ledger tracks costs and payouts, and privacy mode masks every amount. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Trading alerts from TradingView, Amibroker, ChartInk, Excel, or a Python script turn into orders at your own broker through OpenAlgo, a self-hosted trading desk for Indian markets. One REST API handles orders, positions, holdings, funds, quotes, and market depth across more than 35 brokers, including Zerodha, Upstox, Angel One, Fyers, and Dhan, while a WebSocket feed streams live prices. Strategies can be wired together without code in Flow, a drag-and-drop canvas of data, indicator, condition, and order nodes with webhook triggers, or written in Python and scheduled with start and stop times in the built-in editor, each in an isolated process with live logs. The charting terminal places orders straight from the chart, with layouts of up to eight charts, over 100 indicators, drawing tools, and bar replay. Options traders get an option chain with live Greeks, OI tracker, max pain, straddle charts, a 3D volatility surface, a GEX dashboard, and a strategy builder with payoff diagrams. Analyzer mode runs any strategy against real market data with 1 crore rupees of sandbox capital. Semi-auto mode in the Action Center holds each order for your approval, and the AI agent charts, analyzes, and drafts strategies but pauses for sign-off on every order. Telegram alerts, a latency monitor, a P&L tracker, and Historify downloads with CSV and Parquet export cover daily operations. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

A trading strategy that works on your laptop still has to survive restarts, remember its positions, and tell you what it is doing at 3 a.m.; the0 is the self-hosted runtime that handles that last mile for algorithmic trading bots. Write your bot in Python, TypeScript, Rust, C++, C#, Scala, or Haskell with the matching SDK, package it as a reusable custom bot, and instantiate it as many times as you like with different symbols and parameters. Every instance runs in its own isolated container, either continuously for realtime strategies or on a cron schedule for periodic ones, with key-value state that survives between runs and restarts. Structured logs and metrics stream into the dashboard in real time, and you can ship a custom React frontend with your bot so its prices, signals, and positions render exactly the way you want. Deploy from the CLI in one command, drive everything from the REST API, or let an AI assistant list, deploy, and debug bots through the built-in MCP server. Scale from a single Docker Compose host to Kubernetes with the Helm chart, where bots become pods and CronJobs, and keep backtesting and market data in your own research stack, because the0 deliberately runs production strategies rather than simulating them. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Accepted as a NeurIPS 2025 Spotlight paper and rapidly approaching 6,000 GitHub stars, AI-Researcher from the Hong Kong University Data Science Lab delivers the first fully autonomous scientific research system — a multi-agent platform that takes a list of reference papers and returns a complete research contribution with working code, validated experiments, and a formatted academic manuscript. The pipeline orchestrates five distinct phases: a Resource Collector systematically gathers materials from arXiv, IEEE Xplore, ACM Digital Library, Google Scholar, GitHub, and Hugging Face; an Idea Generator performs gap analysis against semantic embeddings to produce 3-5 novel hypotheses with feasibility scores; an Algorithm Designer transforms concepts into functional implementations; a Validation Engine automates testing, performance evaluation, and iterative optimization; and a Manuscript Creator generates polished full-length papers with figures, tables, and citations. The Gradio-based web GUI provides intuitive tabs for environment configuration, example selection, and real-time monitoring of research progress, while the production deployment at novix.science offers immediate browser access without local setup. Scientist-Bench provides a standardized benchmark comprising state-of-the-art papers across diverse AI research domains for evaluating autonomous research capabilities. The system supports multiple LLM providers including OpenAI, Anthropic, Google Gemini, and OpenRouter models with per-task routing for cost optimization. Deploy via Python with pip dependencies or Docker containerization. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

A question goes in; a cited, long-form report comes out - GPT Researcher is an open-source autonomous research agent. A planner agent decomposes the query into sub-questions, execution agents crawl 20+ web sources in parallel with JavaScript-enabled scraping, and a publisher aggregates findings into a 2,000+ word report with inline citations, exportable to PDF, Word, and Markdown. The Deep Research mode extends this recursively: each result yields follow-up questions that are explored to configurable breadth and depth in a tree pattern, while accumulated learnings, citations, and visited URLs are shared across branches. It also researches local documents (PDF, CSV, Word) alongside the web. LLM and search providers are pluggable, including OpenAI, Anthropic, Google, DeepSeek, and Ollama for models, and Tavily, Google, Bing, DuckDuckGo, and SearXNG for retrieval. It ships as a Python package, a FastAPI server with web frontend, a Docker image, and an MCP server for use inside Claude or Cursor. MIT-licensed.

Execute comprehensive multi-angle investigations across scientific journals and web repositories using Local Deep Research, an autonomous intelligence engine designed for researchers, analysts, and knowledge workers. Users can submit complex analytical questions through a self-hosted web console to initiate agentic research cycles powered by LangGraph workflows. The application dynamically queries search providers including SearXNG, arXiv, PubMed, Wikipedia, and Google, iteratively evaluating discovered content to formulate targeted follow-up queries. Discovered academic papers and web sources can be indexed into an encrypted personal library to combine internal documentation with external search intelligence. Reports are synthesized with structured outlines, section headings, and verifiable academic citations that link directly to source references. Individual research sessions, system logs, and user credentials remain isolated in per-user SQLCipher databases protected by AES-256 encryption. Automated subscriptions deliver scheduled news digests and topic monitoring summaries directly to connected notification channels. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Positioned as the open-source NotebookLM alternative for AI agents, SurfSense delivers a live web research platform where your agents access structured data from Reddit, YouTube, Instagram, TikTok, Amazon, Walmart, Google Maps, Google Search, Indeed, and any page on the open web through one REST API or MCP server. Scheduled and event-triggered agents transform findings into briefs, alerts, podcasts, and presentations, while a built-in knowledge base keeps every discovery searchable with Perplexity-style cited answers using hybrid semantic and full-text search powered by PostgreSQL with pgvector. Upload PDFs, Office documents, images, and audio files, or sync Google Drive, OneDrive, and Dropbox — 50+ file formats supported with AI file sorting that auto-organizes documents by source, date, and topic. The MCP server exposes scrapers, knowledge base, and workspaces as native tools for Claude, Cursor, and any MCP-compatible agent. Cross-country proxy rotation handles Reddit, TikTok, and Google Search scraping with geo-aware sticky sessions and captcha-aware anti-bot handling. The platform features collaborative chats, multi-format document export, git-native knowledge base with Open Knowledge Format export, and a desktop quick-ask panel with global shortcut. Docker Compose deployment manages nine services including Caddy proxy, PostgreSQL, Redis, FastAPI backend, Celery workers, zero-cache real-time sync, and Next.js frontend with automatic Watchtower updates. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Insights LM gives you a NotebookLM-style research assistant that runs on your own infrastructure and answers strictly from the documents you feed it. Build a notebook for a project, drop in PDFs, pasted text, website links, and audio files, then ask questions in plain language and get answers grounded in those sources rather than the open internet. Every answer carries citations you can click to jump straight to the passage it came from, so verifying a claim takes seconds instead of a fresh search. Findings worth keeping become notes saved alongside the notebook, building a private knowledge base that grows with your research. When reading is too slow, generate an audio overview and listen to two hosts discuss your material like a podcast episode, perfect for absorbing a long report on a walk. Teams use it to interrogate internal documentation, onboard new hires against company knowledge, and brief themselves before meetings without uploading confidential files to a third-party service. Because the whole stack is yours, you choose which AI models do the reading, including local models for fully private work. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.