Moodfol.io
TraderSync
JournalX
Quantro
TradesViz
Stonk Journal
AntSignals
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Matplotlib
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D3.js
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GnuPlot
Jupyter
Moodfol.io is the fastest trading and emotions journal built to help traders understand not just their performance - but themselves. It combines seamless trade logging, emotion tracking, and AI-driven insights to help you build discipline, consistency, and self-awareness in your trading routine.
Most traders only track numbers. Moodfol.io goes deeper by letting you tag each trade with your emotions (Calm, Focused, Tilted, FOMO, etc.) and strategies (Breakout, Pullback, RSI, News, and more). This reveals the psychological patterns behind your wins and lossesโshowing you when you trade best, and when emotion gets in the way.
Its AI-powered insights work in real time and over time. After every trade, the AI reviews your recent activity to highlight patterns, mindset shifts, and early warning signs of impulsive trading. At the end of each week, youโll receive a personalized AI recap summarizing your results, key lessons, and suggestions to improve your consistency.
Moodfol.io also features screenshot extraction, allowing you to upload a trade screenshot from any broker app - its AI automatically detects and logs your latest trade details (profit/loss, trade type, strategy, etc.) in seconds. No spreadsheets, no manual typing, no missed trades.
Built with React 18, TypeScript, Supabase, Tailwind, and OpenAI, Moodfol.io delivers a smooth, modern experience on both desktop and mobile. Your data stays synced, secure, and private at all times.
Whether youโre trading stocks, crypto, or futures, Moodfol.io helps you track what really drives your performance - so you can trade with awareness, not emotion.
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Moodfol.io's answer
Moodfol.io is the only trading journal designed around both speed and psychology. It lets traders log trades instantly, tag emotions and strategies, and get AI-driven insights that reveal whatโs really driving their results. It transforms journaling into a tool for emotional awareness and consistent performance.
Moodfol.io's answer
Moodfol.io is built for active day traders and swing traders who want to improve not only their strategies but also their psychology. Its users are typically self-improving, data-driven individuals who understand that consistency in trading comes from mastering both numbers and emotions.
Moodfol.io's answer
Moodfol.io was created by a trader who experienced firsthand how emotion can destroy performance. After being liquidated one too many times, he realized the missing piece wasnโt strategy - it was self-awareness. He built Moodfol.io to make journaling fast, insightful, and focused on the emotional side of trading.
Moodfol.io's answer
Moodfol.io is powered by React 18 + TypeScript + Vite for the frontend, Tailwind CSS and shadcn/ui for design, Supabase (Postgres, Auth, Storage) for backend infrastructure, n8n for workflow automation, and OpenAI for trade extraction, emotion analysis, and personalized insights.
Moodfol.io's answer
Moodfol.io serves a growing base of active traders across stocks, crypto, and futures - ranging from independent retail traders to small prop-firm communities. Early adopters include trading coaches, Discord trading groups, and influencers who use Moodfol.io to help their members build better habits and track performance.
Moodfol.io's answer
Unlike complex or purely data-driven journals, Moodfol.io focuses on the human side of trading - mindset, discipline, and emotion. Itโs fast, modern, and built for real traders who want actionable insights without extra friction. Plus, its AI features automate screenshot extraction and weekly analysis, saving hours of manual work.
Based on our record, Matplotlib seems to be more popular. It has been mentiond 114 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 4 months ago
Numbers are useful, but sometimes itโs easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 7 months ago
We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 8 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 10 months ago
TraderSync - Biometric trading journal to trade without emotion
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
JournalX - The professional trading journal for serious traders.
NumPy - NumPy is the fundamental package for scientific computing with Python
Quantro - Track trades.
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
