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.

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.

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.

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.

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.

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.

Wealthfolio is a portfolio tracker offering a complete wealth management platform where financial data never leaves your infrastructure. Built on Rust with an Axum HTTP server, Diesel ORM, and SQLite storage, paired with a React 19 and Vite frontend, version 3.6 tracks stocks, ETFs, mutual funds, crypto, and cash savings across unlimited accounts with unified holdings. The performance engine calculates time-weighted returns (TWR), internal rate of return (IRR), volatility, maximum drawdown, and benchmark comparisons against the S&P 500 and custom indices, scoped per-account or across the entire portfolio. Net worth tracking monitors assets including real estate, vehicles, collectibles, and precious metals alongside liabilities, with stale-valuation warnings for items not updated in 90+ days. The spending module categorizes transactions automatically, supports multi-currency credit cards, builds budgets with monthly targets and rollovers, and generates narrative insights. A built-in AI assistant answers portfolio questions, suggests asset classifications, and imports transactions conversationally — bring your own API key from OpenAI, Anthropic, or use local LLMs. The addon system provides sandboxed extensions with granular permission declarations across 16 capability domains. CSV import maps broker exports with per-broker recipes for Interactive Brokers, Schwab, Fidelity, and dozens more. Market data streams from Yahoo Finance with automatic currency conversion. 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.

Stocks, ETFs, crypto, bonds, precious metals, and cash across every account and currency, in one privacy-first dashboard: Ghostfolio is open-source wealth management software. The deliberate design decision is no brokerage linking: positions enter by manual entry, CSV import, or the REST API, so your holdings never pass through a data aggregator. Performance is measured as return on average investment across Today, WTD, MTD, YTD, 1Y, 5Y, and Max timeframes, with benchmark comparison against indices like the S&P 500, dividend tracking, and allocation breakdowns by asset class, region, and sector. A static X-ray analysis flags concentration and other portfolio risks, and a FIRE calculator projects progress toward financial independence. Multi-currency support converts holdings using historical exchange rates, market data comes from Yahoo Finance and CoinGecko among other pluggable providers, and everything exports back out as CSV or JSON. Built with Angular and NestJS on PostgreSQL and Redis, shipped as Docker images for amd64 and ARM, with a mobile-first PWA interface, dark mode, and a distraction-free Zen mode. AGPL-licensed.

Roughly $1M of development work, open-sourced: Maybe Finance began as a $249/year commercial personal finance product before the company released it all. It aggregates bank accounts, credit cards, loans, investments, crypto, and real estate into a single net worth dashboard with historical trend charts - replacing the spreadsheet that usually glues a whole portfolio together. Transactions are categorized and tagged with rules, with merchant tracking and search across imported or synced activity; budgets track spending by category against plan; and the investment view follows holdings, cost basis, and returns across brokerage accounts. Multi-currency support converts accounts held in different currencies into a single reporting currency, bank synchronization works through Plaid where supported, and manual CSV import covers any institution. An optional AI assistant answers questions grounded in your own financial data. Because the app was built as a paid product with professional design before being open-sourced, its interface quality exceeds most community finance tools - and self-hosting means your balances and transactions are not monetized by a free app or gated behind an annual subscription. The stack is Ruby on Rails with Hotwire on PostgreSQL, licensed AGPL-3.0 and deployed via Docker. The original repository is archived; development continues in the community fork Sure, compatible with the same self-hosted setup.

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.

Powering trading floors at firms processing millions of market data events per second and trusted by organizations including Airbus, Yahoo, and Copenhagen Atomics, QuestDB has earned over 17,000 GitHub stars as the time-series database that refuses to make you choose between ingestion speed and query latency. The column-oriented, time-partitioned storage engine processes millions of rows per second on ingest through write-ahead logging with instant durability, while SIMD-accelerated parallel execution returns analytical queries over billions of rows in milliseconds. Time-series SQL extensions add ASOF JOIN for point-in-time lookups across tables with misaligned timestamps, SAMPLE BY for downsampling at arbitrary time intervals, LATEST ON for last-value queries, and WINDOW JOIN for sliding window aggregations — all through standard SQL syntax accessible via the PostgreSQL wire protocol, REST API, or the QuestDB Wire Protocol (QWP) that streams Apache Arrow at 220 million rows per second. Multi-tier storage automatically moves data from the WAL through native columnar format to Parquet files on S3-compatible object storage, keeping hot data fast and cold data portable without manual tiering. The built-in web console provides an interactive SQL editor with syntax highlighting, autocomplete, and charting for quick data exploration. Views, materialized views, and n-dimensional arrays support real-time analytics, while native Parquet export enables direct integration with Python, DuckDB, and AI/ML frameworks. 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.

The de facto dashboard of observability: Grafana is the open-source frontend that turns the data stores you already run into interactive graphs. It does not store metrics itself; it connects to the data stores you already run and turns their contents into interactive dashboards. Supported sources number over 150 via plugins: Prometheus, Loki, Tempo, InfluxDB, Elasticsearch, MySQL, PostgreSQL, Microsoft SQL Server, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring, and many more. Dashboards are built from a large library of panel types (time series, heatmaps, tables, gauges, logs) with template variables for reusable, parameterized views. Unified alerting evaluates rules against any connected data source, not just Prometheus, and routes notifications to Slack, PagerDuty, email, and other channels with grouping and silencing - unlike Prometheus Alertmanager, a single rule can combine a Loki log pattern, a PostgreSQL query result, and a CloudWatch metric. Dashboards serialize to JSON and data sources configure via provisioning files, so the entire observability setup can live in Git and deploy repeatably across environments. Explore mode adds ad-hoc querying outside dashboards, with split view for correlating a metric spike against the matching log lines, and access control spans organizations, teams, folder permissions, and OAuth, LDAP, and SAML integration. Written in Go and TypeScript, AGPL-licensed. Self-hosting gives you unlimited users, dashboards, and queries at flat hosting cost, without Grafana Cloud's usage-based pricing.