trading/docs/plans/remaining-work-plan.md

Trading Bot — Remaining Work Plan

Created: 2026-02-25 Status: Complete

Overview

This plan addresses all remaining TODOs, placeholders, and incomplete features in the trading bot. Tasks are ordered by dependency — earlier tasks unblock later ones.

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Task 1: Learning engine — expand default weights to all 9 strategies

Status: [x] Complete Files: services/learning_engine/main.py

Problem: _load_strategy_weights() defaults to 3 strategies (momentum, mean_reversion, news_driven) but the signal generator uses all 9. When Redis has no cached weights, the learning engine operates on a stale subset.

Fix: Change the default fallback to all 9 strategies with equal weights (~0.111 each), matching scripts/seed_strategies.py.

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Task 2: Portfolio sync — compute actual daily P&L

Status: [x] Complete Files: services/api_gateway/tasks/portfolio_sync.py

Problem: daily_pnl is hardcoded to 0.0 in _sync_once() (line 60).

Fix: Before inserting the new snapshot, query the most recent prior snapshot for today. Compute daily_pnl = current_total_value - day_start_total_value. If no prior snapshot exists for today, the first snapshot's daily_pnl is 0.0.

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Task 3: Portfolio API — cumulative P&L from first snapshot

Status: [x] Complete Files: services/api_gateway/routes/portfolio.py

Problem: total_pnl on line 90 just returns latest.daily_pnl instead of the cumulative P&L since inception.

Fix: Query the earliest PortfolioSnapshot and compute:

• total_pnl = latest.total_value - earliest.total_value
• total_pnl_pct = total_pnl / earliest.total_value * 100

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Task 4: Portfolio API — read trading pause flag from Redis

Status: [x] Complete Files: services/api_gateway/routes/portfolio.py

Problem: trading_active is hardcoded to True (line 92).

Fix: Read the trading:paused key from Redis (same key used by services/api_gateway/routes/controls.py). Return trading_active = not paused.

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Task 5: Portfolio metrics — compute max drawdown from snapshots

Status: [x] Complete Files: services/api_gateway/routes/portfolio.py

Problem: max_drawdown is hardcoded to 0.0 (line 170).

Fix: Query all PortfolioSnapshot rows, compute the running peak and maximum percentage drop from peak. Return as a positive decimal (e.g. 0.12 = 12% drawdown).

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Task 6: Portfolio metrics — compute avg hold duration from trade outcomes

Status: [x] Complete Files: services/api_gateway/routes/portfolio.py

Problem: avg_hold_duration is hardcoded to "0h" (line 172).

Fix: Query TradeOutcome rows, average the hold_duration_seconds column, and format as a human-readable string (e.g. "4h 30m", "2d 6h").

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Task 7: Trade executor — respect the trading pause flag

Status: [x] Complete Files: services/trade_executor/risk_manager.py

Problem: The controls API sets/clears trading:paused in Redis, but the trade executor's RiskManager.check_risk() never checks it. Pausing has no effect.

Fix: Accept a Redis client in RiskManager.__init__(). Add a check at the top of check_risk() that reads trading:paused and rejects with "trading_paused" if set. Wire the Redis client through from trade_executor/main.py.

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Task 8: Learning engine — resolve placeholder strategy_id

Status: [x] Complete Files: services/learning_engine/main.py

Problem: WeightAdjustment on line 207 uses strategy_id=UUID(int=0) as a placeholder. This means weight history records can't be linked to the correct strategy.

Fix: On startup, load the strategies table into a name -> UUID lookup dict. Use this lookup when building WeightAdjustment records. Fall back to UUID(int=0) only if the strategy name isn't in the DB.

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Task 9: Pass strategy_sources from signal through to learning engine

Status: [x] Complete Files: services/trade_executor/main.py, services/learning_engine/main.py

Problem: TradeExecution doesn't carry strategy_sources. When the learning engine stores the opening trade (line 136), strategy_sources is always []. This means credit attribution (evaluator.attribute_credit()) has no strategies to reward.

Fix:

1. Add strategy_sources: list[str] = [] to the TradeExecution schema.
2. In the trade executor's process_signal(), copy signal.strategy_sources into the execution message.
3. In the learning engine, read trade.strategy_sources (via the extended schema) and store them in the opening trade record.

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Task 10: Update CLAUDE.md and add top-level README

Status: [x] Complete Files: .claude/CLAUDE.md, README.md

Fix:

• Remove "No CI/CD pipeline yet" from Known Issues (Woodpecker exists).
• Update strategy count references (3 → 9) in Project Structure section.
• Update model count (14 → 16 tables).
• Create a concise top-level README.md with: project description, architecture diagram (text), quickstart (docker compose up), dev setup, and test commands.

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Execution Order

Tasks are grouped by dependency:

Group A (independent, do first):

• Task 1 (learning engine weights)
• Task 7 (trade executor pause flag)
• Task 9 (strategy_sources passthrough)

Group B (depends on nothing, can parallel with A):

• Task 2 (portfolio sync daily P&L)
• Task 8 (learning engine strategy_id lookup)

Group C (depends on Task 2):

• Task 3 (cumulative P&L API)
• Task 4 (trading_active flag API)
• Task 5 (max drawdown API)
• Task 6 (avg hold duration API)

Group D (after all code changes):

• Task 10 (documentation)

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Test Strategy

After each task, run the relevant unit tests:

python -m pytest tests/ -v -m "not integration" --tb=short

Existing tests should continue to pass. New tests may be needed for:

• Task 7: risk manager pause check
• Task 9: strategy_sources in TradeExecution schema