Designing the data model for a trading journal

Every trader I know starts journaling in a spreadsheet, and every one of them abandons it within a month. Not because journaling is useless, but because the spreadsheet can't answer the questions that actually matter: "which of my rules costs me the most money?" or "am I cutting winners too early?" A flat sheet of rows can't tell you. Tools like RizeTrade solve this with a proper data model, and in this post I'll walk through how to design one yourself. The problem is almost always the data model. If you design the schema well up front, the hard reports fall out of it for free. Here's how I'd structure it. * Start with the trade, but don't overload it * The instinct is to make one giant trades table with fifty columns. Resist it. A trade has a small core of facts: CREATE TABLE trades ( id BIGSERIAL PRIMARY KEY , symbol TEXT NOT NULL , side TEXT NOT NULL CHECK ( side IN ( 'long' , 'short' )), entry_price NUMERIC NOT NULL , exit_price NUMERIC , quantity NUMERIC NOT NULL , entry_at TIMESTAMPTZ NOT NULL , exit_at TIMESTAMPTZ , stop_loss NUMERIC , take_profit NUMERIC , commission NUMERIC DEFAULT 0 , strategy_id BIGINT REFERENCES strategies ( id ) ); Everything else (emotions, notes, screenshots, rule checks) hangs off this in separate tables. That separation is what lets you slice the data later without schema migrations every time you want a new report. * R multiples belong in the model, not the UI * The single most useful number in trading isn't dollar P&L. It's the R multiple: profit or loss expressed in units of the risk you planned to take. A +2R win and a +2R win are comparable across a $500 account and a $50,000 account; two dollar figures aren't. Planned risk is defined the moment you enter, by your stop: planned_risk_per_unit = |entry_price - stop_loss| planned_R = (exit_price - entry_price) / planned_risk_per_unit (for longs) Store the stop at entry time. If you only record the realized exit, you lose the ability to detect stop loss violations, which is the who