How should I structure a prediction market trading journal?
Record each trade at three points: before entry (market, side, size, price, your probability, the thesis and what would prove it wrong), at execution (quoted against actual fill, fees, slippage), and after resolution (result and lesson). Keep it short enough that you still fill it in after a losing day.
The generator builds a markdown template and a CSV header row for your strategy and review depth, with review prompts for after the trade.
- Best for
- Standardizing trade notes and post-trade review
- Primary output
- Copy-ready journal template and CSV header row
- Use before
- Starting a new trading process or log format
Key points
- Builds a markdown template and a CSV header row
- Fits directional, arbitrage, event-driven, sports and hedge trades
- Adds review prompts for after resolution
Method and assumptions
Why keep a journal when you already track PnL
PnL records what happened to the money. A journal records the decision: what you expected, why, and how the trade filled. Without it, you cannot tell a good decision from a lucky one.
In prediction markets, execution, rule review and sizing often explain results as much as the thesis does. The journal keeps them on record.
- Separates good process from lucky outcomes
- Keeps the thesis as written before hindsight
- Makes review possible across many trades
- Shows repeated execution and sizing mistakes
How to build a journal you will keep
Start with the fields you need to understand a trade later: market, side, size, price, thesis and invalidation. Add a module only if you will review it.
Fields depend on the strategy. An arbitrage needs both venues and the execution detail; an event-driven trade needs the catalyst and its source.
- Choose the strategy type that best matches the trade.
- Pick a workflow depth you can sustain after winning and losing streaks.
- Turn on only the optional sections that improve actual review quality.
- Copy the markdown template and structured CSV header into your system.
Worked journal example
A directional trade with a full lifecycle review gets three blocks. Trade setup records the market, venue, side, entry price, size, thesis, invalidation, and your probability against the market’s. The execution log compares quoted and actual fill price. The post-trade review records the exit, realized PnL, what happened and a process grade.
An arbitrage template adds both leg venues and prices, a target net edge, and a resolution review of the official source, cutoff time and timezone. One template per strategy keeps each journal complete without padding the others.
- Arbitrage logs need more execution detail
- Directional trades need a clear thesis and invalidation
- Review prompts make you interpret the trade, not just log it
Journal mistakes to avoid
Logging only outcomes. A record of who won and how much you made cannot show whether the thesis or the fill was wrong.
Making the template too heavy, or skipping it after bad trades. The trades you least want to write up usually teach the most.
- Tracking only PnL and not the decision process
- Using one rigid template for every strategy type
- Building a journal too heavy to keep current
- Skipping notes on the trades that went wrong
Sources and assumptions
Frequently asked questions
Should I keep my journal in markdown, a spreadsheet, or a database?
Use the system you will maintain. Many traders keep markdown notes for context and a CSV or spreadsheet for structured review. This generator supports both outputs for that reason.
Do I need a different journal for arbitrage versus directional trades?
Usually yes. The core fields overlap, but arbitrage trades often need venue, parity, and execution-specific details that a simple directional template does not.
How many fields is too many?
Too many is any number that causes you to stop updating the journal after a busy or emotional session. The template should serve review, not compete with trading for attention.
Why does the generator include review prompts?
Because structured reflection is what turns a log into a process tool. The prompts help you diagnose repeatable mistakes instead of only recording results.