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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Good European market data is either expensive or wrong, so Fonrex runs it all on your server: a self-hosted API serving per-listing prices, cross-checked fundamentals, DCF valuations, and factor models for European and US shares, with no subscription or per-call meter. Every price is stored per listing in its own currency and dated by trading session, so AIR.PA resolves to Airbus rather than a same-ticker US namesake, and one ISIN can carry many listings cleanly. Fundamentals are cross-checked between sources first; figures that disagree with the consensus are rejected, and every answer names the sources it came from. Value any company three ways with FCF, EPS, and dividend DCF models, compare them side by side, and stress the result with a WACC by growth sensitivity matrix. Factor tools return Kenneth French returns for the US, Europe, and developed markets, compute listing factor exposure, and price equity with the risk-free rate of the right currency, alongside FRED and ECB rates, Treasury curves, and Eurostat indicators. Daily history spans ten years, split-adjusted with a dividend-adjusted close; live ticks stream over WebSocket, indicators and a screener run on your stored prices, and deduplicated news arrives per ticker or as a global feed. Results plug into OpenBB Workspace, Google Sheets, Python notebooks, and Zipline backtests, so spreadsheets, dashboards, and research pipelines all read the same numbers. 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.

Exploratory data work, teaching material, and research code all live in the same browser tab with JupyterLab, the web workspace of Project Jupyter. Notebooks combine runnable code cells, Markdown, and rich output such as images, JSON, and Vega charts, and they use the same notebook format as the classic Jupyter Notebook. Any Jupyter kernel can execute the code, so Python, R, and other languages share one interface, and code inside plain text files such as Markdown or LaTeX can be run interactively as well. Tabs and splitters arrange documents side by side, and a notebook's cell outputs can be mirrored into their own tab to build a simple dashboard with interactive controls. Code consoles serve as scratchpads linked to a notebook kernel, while full terminals run system shells such as bash on the server. A visual debugger offers breakpoints, a call stack, and a variable explorer for kernels that support the Jupyter debug protocol. Markdown, CSV, and Vega documents show a live preview as they are edited, and built-in viewers handle images, JSON, and PDF files. The Extension Manager installs themes, editors, and renderers from PyPI, and real-time collaboration becomes available by adding the jupyter-collaboration extension. Access is protected by a generated token unless a password is set. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. BSD-3-Clause licensed.

Running one marimo notebook is easy; running a team's worth of them, with storage, sign-on, versions, and somewhere for the results to live, is the job MarimoHub takes on. Notebooks gather in a searchable gallery organized by project, where every edit session leaves a version you can compare side by side, inspect, and restore when an experiment takes a wrong turn. Projects connect their data sources once through a versioned integration catalog, with sensitive values encrypted or pulled from external secret stores, and every new session receives the configuration automatically. When a notebook is ready for people who should never see its code, publish it as an interactive app: viewers get the controls and outputs while rollouts shift versions underneath them. Scheduled jobs rerun notebooks on a clock, GitHub sync keeps repository-backed notebooks current, and an assistant can work inside the notebook context with managed AI keys that never reach the sandbox. Underneath, you pick the object storage, the compute, and the identity provider, and back up one bucket to back up the whole hub. 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.

Training runs stop being terminal scrollback and become a board you can read at a glance. SwanLab is an open-source experiment tracking and visualization platform for AI model training that records metrics, hyperparameters, logs, and hardware signals from your training code into a modern web dashboard you can self-host. A few lines of Python start an experiment, and the dashboard follows loss curves, learning rates, and custom scalars live, with line chart configuration, smoothing, grouping, and detailed hover inspection for comparing runs. Experiments organize into projects and groups with tags, pinning, filtering, and a table view that sorts and aligns runs against a baseline with difference percentages. Media logging goes far beyond scalars: images, audio, video, text with Markdown rendering, 3D point clouds, molecular structures, and custom ECharts visualizations including PR curves, ROC curves, and confusion matrices. Hardware monitoring records GPU, CPU, memory, disk, and network behavior from NVIDIA, AMD, Ascend, and other accelerators alongside the metrics they produced. Integrations cover more than fifty frameworks, from PyTorch Lightning and Hugging Face Transformers to LLaMA Factory, veRL, and XGBoost, and existing histories migrate in through sync tools for MLflow, TensorBoard, and Weights and Biases. Teams collaborate with organizations, roles, project sharing, webhooks, and notification plugins. 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.