# Trade Debrief > Educational review of past trading sessions. This skill combines timeline_report, performance_summary, fill_history, position_history, and market_history into a structured report. It then routes the report through 7 prompt templates. These are mistake identification, pattern extraction, trade scoring, correction rules, daily debrief, pattern clustering, and expectancy analysis. Every output cites the report fields directly and leads with the dollar impact. This skill describes the user's own past trading patterns. It labels any proposed change as a hypothesis, not advice. Use it for "debrief", "analyze trading", "what mistakes did I make", "what went wrong", "find patterns in trades", "score a trade", "review today's session", "coaching", "improvement rules", "what should I change", or "was that a good trade". For plain "how did I trade today" summaries, or for descriptive TCA, use trade-journal. For single-trade counterfactuals, use trade-replay. - Skill: `nt-ninjatrader/trade-debrief` (Agent Skill, multi-file: 15 files) - Install (CLI): `npx skillmds@latest add nt-ninjatrader/trade-debrief` - Raw SKILL.md: https://api.skillmd.com/api/skills/nt-ninjatrader/trade-debrief/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: NT-NinjaTrader (https://skillmd.com/u/nt-ninjatrader) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/nt-ninjatrader/trade-debrief --- # trade-debrief ## Purpose Synthesize session data into a coaching-grade debrief with the bundled prompt library. Descriptive TCA lives in `trade-journal`. This skill is prescriptive. It surfaces mistakes and patterns, and proposes rules and measurable improvements. ## Environment routing Demo (simulation) and live are two separate MCP servers. Account names are unique to one server. Once a workflow resolves an account on a server, every downstream call must go through that same server. This includes `my_portfolio`, `market_snapshot`, `place_order`, `create_alert`, history tools, etc. Cross-routing fails or hits the wrong environment. ## MCP tools used Tool names below are bare. The NinjaTrader MCP server provides them. Your client adds its own prefix. See `AGENTS.md` at the repo root. - `timeline_report` — per-session event timeline (single tool post-consolidation: `account`, `date`, optional `endDate` for a range) - `performance_summary` — arrays of `{name, value}` stat pairs under `extra.allTradeStats[]` / `extra.profitTradeStats[]` / `extra.lossTradeStats[]` (e.g. `{name: "Total P/L", value: "$765.00"}`), plus `extra.tradeLog[]` and `extra.hasTrades` - `fill_history` — fill-level timestamps - `position_history` — closed-position summaries - `market_history` — bar OHLC for MFE/MAE - `search_contracts` — contract metadata (`productName`, `tickSize`, `valuePerPoint`) - `economic_calendar` — event context (via `event-watch` skill, not this one directly) - `user_profile` — discover available account names when the account is unknown ## Skills consumed - `trade-journal`'s `streaks.py` — FIFO round-trip pairing over `fill_history`, to produce the `trades[]` list. Reuse it. Do not duplicate it. - `market-context` — session VWAP, ATR, and regime narrative per symbol, for the `market_context` block of each trade. ## Workflow ### 1. Classify the intent → pick the prompt `references/prompt-selection.md` has the full routing table. Rough mapping: | Intent | Prompt | |--------|--------| | Quick wrap of today | `07_daily_debrief` | | "What mistakes" | `01_mistake_identification` | | "Find patterns" | `02_pattern_extraction` | | "Score my trades" | `03_trade_scoring` | | "What rules" | `06_correction_rules` | | Cross-day clustering | `08_pattern_clustering` | | Edge / expectancy | `09_expectancy_analysis` | ### 2. Gather MCP inputs (in parallel) **Account resolution.** All MCP calls below require `account=`. If the account name is unknown, call `user_profile()` first. Its `accounts[].name` field lists every account on this MCP server. ``` timeline_report(account=..., date=, endDate=) performance_summary(account=..., startDate=, endDate=) fill_history(account=..., startDate=, endDate=) position_history(account=..., startDate=, endDate=) search_contracts(text=, includeFamilySiblings=false) ``` Date params are date-level. They use `YYYY-MM-DD` or a natural term like `"last week"`, never a timestamp. Then for each trade, fetch a bar window around its lifetime: ``` market_history(symbol=, barType="Minute", barSize=1, from=, to=) ``` (`from` and `to` are full ISO-8601 timestamps. `from` requires `to`.) Keep per-trade bar fetches bounded. Unless the user asks for per-minute pattern analysis, do not fetch 1-min bars for a full day (expensive). ### 3. Pair fills into trades Run `trade-journal`'s `streaks.py` over the fill list. Use its output round-trips as the trade list. Attach each trade's corresponding bar window to it. ### 4. Compute derived metrics Save the trades-with-bars payload to a file. Pass its path with `--file`. Never re-type or inline a large JSON payload in the command. ```bash python3 scripts/compute_derived.py --file trades_with_bars.json ``` Per trade, it produces `realized_points/dollars/R`, `mfe_points/dollars/R`, `mae_points/dollars/R`, `giveback_from_mfe_pct`, and `realized_vs_mfe_pct`. Some fields are always null: `scale_out_count`, `stop_modified_count`, and minute-by-minute timeline volume features. The MCP cannot source them. See `references/report-schema.md` for the full provenance map. ### 5. Assemble the report YAML ```bash python3 scripts/assemble_report.py --file combined_input.json ``` It takes `account_id`, `trade_date`, `timeline_report`, `performance_summary`, `trades` (post-`compute_derived`), a `contracts` map, and an optional `market_context` map. It emits the YAML shape that the prompts expect. See `references/report-schema.md` for details. ### 6. Run the prompt (internally — Claude IS the LLM) 1. Read the chosen prompt file (e.g., `references/prompts/07_daily_debrief.md`). 2. Append the assembled YAML below the prompt's final `## Trade Timeline Report` header. 3. Follow the prompt's STRICT RULES. These require fact-only content, YAML-path citations, and the mode-specific output structure. 4. When a rule says "cite exact YAML path", cite only fields that have a value, not fields that are null. `references/report-schema.md` lists which fields are null today. **Do not** route this through an external LLM API. Claude Code IS the LLM. Just read the prompt and apply its constraints. ### 7. Echo a summary + point at the raw report End with a 1-line pointer: "Full assembled report YAML available on request; source tools: `timeline_report`, `performance_summary`, `fill_history`, `market_history`, `search_contracts`." This gives the user transparency about where the numbers came from. ## Output constraints (from the prompt library) - **Fact-only with citations.** Every claim references a YAML path. No speculation. - **Dollar-first, R-second.** Lead with "$875 left on the table", then "0.7R giveback" for context. - **Hypotheses, labeled explicitly.** If a claim needs data the report does not have yet, mark it "hypothesis" and state the evidence it needs. - **No invented indicators.** Only use fields present in the assembled report. Volume and POC features are null today. Do not cite them. ## Output idioms Prompt 07 example (daily_debrief), roughly 200 words: > **Performance Summary** > Net +$765 / +1.2R across 2 trades on the E-Mini S&P 500 and > E-Mini NASDAQ-100 (`summary.total_pnl_dollars = 765.00`). Win rate > 100% (2 of 2), but MFE capture lagged on both. > > **Primary Pattern** > Systematic MFE giveback. ES gave back 47.5% from peak > (`trades[0].derived.giveback_from_mfe_pct = 47.5`); NQ gave back 40% > (`trades[1].derived.giveback_from_mfe_pct = 40.0`). Combined $600 > left on the table from peak. > > **Top 3 Improvements** > 1. **Rule:** `IF mfe_R >= 2.0 THEN scale_out_50pct` > - **Evidence:** `trades[0].mfe_R = 2.5`; exit at 1.3R (realized > = $525, peak = $1,000, giveback = $475) > - **Expected impact:** +$475 / +1.2R improvement on ES > 2. **Rule:** `IF mfe_R >= 0.5 AND time_in_trade > 10min THEN move_stop_to_breakeven` > - **Trades affected:** both > 3. (Hypothesis; needs scale-out action data not yet in report.) ## Disambiguation - **vs `trade-journal`**: journal reports descriptive TCA and streaks — what happened. Debrief mines prescriptive rules — what to change. - **vs `trade-replay`**: replay does one trade's deep dive, with counterfactual exits. Debrief synthesizes cross-trade patterns at the session level. - **vs `risk-coach`**: coach handles live behavior, such as tilt, revenge trading, and overtrading. Debrief handles retrospective mechanics, such as giveback, scale discipline, and stop hygiene. - **vs `market-context`**: debrief consumes it as input for the `market_context` block of each trade. It is not a substitute for debrief. ## Explicit non-goals - **No external LLM round-trip.** Claude Code IS the LLM. - **No modification of the bundled prompts.** This skill uses them verbatim. - **No fabricated fields.** `timeline[n].participation_rate_vs_session`, `poc_shift`, `scale_out_count`, and `stop_modified_count` are null today. Do not guess their values. - **No daily auto-push.** This is a reactive skill. It runs only when the user asks. ## Resource layout - `scripts/compute_derived.py` — per-trade MFE/MAE + giveback metrics from bar scans. Shared pattern with `trade-replay`. - `scripts/assemble_report.py` — combines compute-derived trades + contracts + performance summary + market context → the assembled report YAML. - `scripts/fixtures/trades_with_bars_synthetic.json` — 2-trade multi-symbol fixture (ES long winner, NQ short winner with significant MFE giveback) for smoke tests. - `references/prompts/` — 7 prompt template files: `01_mistake_identification.md`, `02_pattern_extraction.md`, `03_trade_scoring.md`, `06_correction_rules.md`, `07_daily_debrief.md`, `08_pattern_clustering.md`, `09_expectancy_analysis.md`. Load only the one file that matches the chosen prompt. - `references/prompt-selection.md` — intent-to-prompt routing + combinations + how-to-run inside Claude Code. Load it when you classify the intent and choose a prompt. - `references/report-schema.md` — field provenance map (MCP-sourced vs derived vs null-today), per-trade block reference, summary block reference. Load it when you check which fields the chosen prompt needs.