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JournalX is a trading journal for active, self-directed traders who want a real feedback loop on their performance. Log, plan, and analyze every trade in one workspace, then use your own data to work out what is working in your strategy and what is not.
Most journals are a record of what already happened. JournalX starts one step earlier. You write the plan before you take the trade (setup, entry, stop, target, size), take the trade, and the journal reconciles the plan against the execution. Over a few hundred trades, that gap between what you planned and what you did becomes the most useful data you have, and almost nothing else measures it.
Your strategy lives in the product as Gameplans: reusable entry and risk rules you define once, attach to trades, and review for adherence. Pair that with a filter builder that stacks conditions (setup, session, symbol, tag, win or loss) and you can answer a specific question instead of staring at one blended P&L number. An AI assistant works over your own trade history, so you can ask why a month went badly and get an answer grounded in your trades rather than a generic tip.
Trades arrive by broker auto-sync, CSV import with column mapping, or manual entry. Stocks, options, futures, forex, and crypto are all supported, with multi-account and multi-currency tracking.
JournalX iPythonJournalX's answer
JournalX started from a problem most traders recognise. You keep a spreadsheet, it grows to forty columns, and at some point you quietly stop updating it. Even when you do keep it current, it tells you what your P&L was without telling you why.
The existing journals solved half of that. They import trades and produce charts. But they still only look backward, at decisions already made and no longer changeable. The idea behind JournalX was that the interesting data is the difference between the trade you planned and the trade you actually took. Capture the plan first and the journal can show you where discipline broke down, not just where money was lost.
So the product was built around that loop: plan, execute, reconcile, review. Everything else, the analytics, the Gameplans, the notes, the AI assistant, the broker sync, exists to make that loop fast enough to run every single day.
JournalX's answer
Most trading journals are a record of what already happened. JournalX starts one step earlier.
You write the plan before you take the trade: setup, entry, stop, target, position size. Then you take the trade, and the journal reconciles the plan against what you actually did. Over a few hundred trades, that gap between the trade you planned and the trade you took is usually where the real problem lives, and almost no other journal measures it.
JournalX's answer
Three reasons.
Pre-trade planning. TradeZella, TraderSync, Tradervue, and Edgewonk are all capable at post-trade analysis. None of them ask you to commit to a plan before entry and then score you against it. That is the habit that actually changes behaviour, and it is what JournalX is built around.
Speed and interface. A journal only works if you use it every day. JournalX is fast, navigable from the keyboard through a command palette, and designed so a daily review takes minutes rather than becoming the chore you skip.
Price and coverage. Starter is $24/mo, or $19/mo billed annually. Pro is $49/mo, or $29/mo billed annually. Stocks, options, futures, forex, and crypto are supported at every tier, with broker auto-sync, CSV import, and manual entry, and there is a 7-day free trial.
JournalX's answer
Active, self-directed retail traders who treat trading as a craft and want a real feedback loop on their performance. That spans day and swing traders, and futures, options, forex, and crypto traders across global markets.
Based on our record, iPython seems to be more popular. It has been mentiond 20 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.
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 / 11 months ago
As alluded to in Poetry2Nix Development Flake with Matplotlib GTK Support, Iโm currently in the process of getting my โnewโ python workflow up to speed. My second problem, after dependency and environment management, was that fancy REPLs like ipython or ptpython donโt jazz well with the standard comint based inferior python repl that comes with python-mode. One can basically only run ipython with the... - Source: dev.to / about 2 years ago
Third, if possible use a command line interpreter to test things out. I recommend ipython for this purpose. You can use your browser's developer console this way if you are learning Javascript. Source: over 3 years ago
IJulia is an interactive notebook environment powered by the Julia programming language. Its backend is integrated with that of the Jupyter environment. The interface is web-based, similar to the iPython notebook. It is open-source and cross-platform. - Source: dev.to / over 3 years ago
Also, take a look at installing iPthon to give you a much richer shell environment. This underpins Jupyter Notebooks, so is well known, proven and trusted. Source: over 3 years ago
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