Decision Dashboard

Build a batch decision dashboard for: $ARGUMENTS

Overview

Implementation status: code-backed
Local entry script: <bundle-root>/decision-dashboard/run.py
Primary purpose: summarize conditional actions, scores, and regimes across a symbol set instead of returning one-symbol reports
Research layer: batch decision support (Stage 6: Risk Management & Position Sizing - Portfolio-level view)
Workflow stages: stage 1 Universe Review and stage 6 Risk Management & Position Sizing in batch form
Local executor guarantee: compute per-symbol decision summaries under shared account assumptions, aggregate action counts, and return a ranked focus list

Use When

The user wants a batch decision board rather than raw ranking.
The user wants action distribution across a watchlist.
The user wants to shortlist names before moving into one-symbol detail.
The user wants portfolio-level view of potential actions.
The user wants to understand aggregate exposure and risk across watchlist.
The user wants daily monitoring dashboard for existing positions and watchlist.

Do Not Use When

The user wants a complete report for one symbol. Use market-brief or analysis.
The user only wants ranked screening output. Use market-screen.
The user wants only watchlist ingestion. Use watchlist-import.
The user expects this to produce a portfolio optimizer, cross-name correlation control, or live order book. It does not.
The user wants actual portfolio construction with correlation and diversification. This skill does not model cross-name correlations.

Inputs

Direct positional symbols.
Optional --file PATH: plain text file containing one symbol per line.
Optional --dir PATH: directory of per-symbol CSV files.
Optional --capital, --cash, --risk, --max-position: influence dashboard decision framing.
Optional --top N, --start, --end, --source.
Important boundary: --file is a symbol-list file, not a market-data CSV.
Assumption note:
- the same account assumptions are applied across the batch
- the agent should identify when those assumptions are user-supplied versus defaulted

Execution

Step 1: Define dashboard scope and portfolio context

Before building dashboard, clarify objectives and constraints:

Dashboard objectives:

Daily monitoring (track existing positions and watchlist)
Idea triage (identify new opportunities from watchlist)
Position review (assess current holdings for hold/reduce/exit)
Risk review (identify concentration, exposure, risk flags)
Action planning (prioritize symbols for deeper analysis)

Portfolio context:

Total capital available
Current cash balance
Existing positions (symbols, quantities, cost basis)
Risk tolerance (max position size, max portfolio risk)
Concentration limits (max % per position, per sector)

Shared assumptions:

Capital: Total portfolio value (default: user-supplied or session default)
Cash: Available cash for new positions (default: 100% of capital if no positions)
Risk per position: Max % of capital per position (default: 5-10%)
Max position count: Max number of positions (default: 10-20)
Rebalancing threshold: When to adjust positions (default: ±20% from target)

A-share specific constraints:

T+1 settlement (cannot sell same-day purchases)
Lot size (100 shares minimum)
Price limits (±10% or ±20%)
Suspension risk
Margin requirements (if using margin)

Step 2: Validate universe and collect data

Process watchlist with quality validation:

Universe validation:

All symbols are valid A-share tickers
Symbols are currently tradeable (not delisted)
Symbols are not suspended (or suspension flagged)
Symbols have sufficient data history (minimum 60 days)
Symbols meet minimum liquidity requirements

For each symbol, collect:

Market data (OHLCV, normalized and validated)
Technical features (trend, momentum, volatility, volume)
Market state (score, regime, bias, risk flags)
Current position (if held: quantity, cost basis, P&L)
Liquidity metrics (volume, turnover, bid-ask spread)

Partial failure handling:

Skip invalid symbols and record reason
Skip suspended symbols and flag for monitoring
Skip insufficient data symbols and record reason
Continue processing remaining symbols

Step 3: Generate per-symbol decision summaries

For each valid symbol, generate conditional action:

Decision logic (consistent with decision-support):

For symbols NOT currently held:

Buy (score 70-100, bullish bias, no major risk flags):
- Suggested position size (% of capital)
- Entry price range
- Initial stop-loss level
- Confidence level
Watch (score 50-69, neutral/uncertain bias, or minor risk flags):
- Reason for watching (waiting for confirmation, better entry, etc.)
- Trigger conditions (what would change this to Buy)
Avoid (score 0-49, bearish bias, or major risk flags):
- Reason for avoiding (downtrend, overbought, high risk, etc.)

For symbols currently held:

Hold (score 60-100, position within target range, no major risk flags):
- Current position size vs. target
- Unrealized P&L
- Stop-loss level
- Monitoring conditions
Reduce (score 40-59, position above target, or moderate risk flags):
- Suggested reduction amount (shares or %)
- Reason for reduction (risk management, rebalancing, etc.)
- Target position size after reduction
Exit (score 0-39, major risk flags, or stop-loss triggered):
- Reason for exit (stop-loss, invalidation, risk, etc.)
- Expected exit price
- Realized P&L (if exited)

Position sizing (for Buy actions):

Fixed sizing: Equal weight across all positions (e.g., 5% each for 20-position portfolio)
Score-based sizing: Higher score = larger position (within limits)
Risk-based sizing: Size based on volatility (higher vol = smaller position)
Liquidity-constrained: Limit position to % of ADV (e.g., max 5% of 20-day ADV)

Risk checks:

Position size within limits (max % per position)
Total exposure within limits (max % invested)
Concentration within limits (max % per sector, if tracked)
Cash sufficiency (enough cash for suggested buys)
Liquidity sufficiency (can execute without excessive impact)

Step 4: Aggregate action distribution

Summarize actions across entire watchlist:

Action counts:

Buy: Count of symbols with Buy recommendation
Watch: Count of symbols with Watch recommendation
Avoid: Count of symbols with Avoid recommendation
Hold: Count of currently held symbols with Hold recommendation
Reduce: Count of currently held symbols with Reduce recommendation
Exit: Count of currently held symbols with Exit recommendation

Action distribution analysis:

% of watchlist in each action category
Trend over time (if historical dashboards available)
Comparison to previous dashboard (if available)

Aggregate exposure:

Total suggested new capital deployment (sum of Buy position sizes)
Total suggested capital reduction (sum of Reduce/Exit position values)
Net capital change (deployment - reduction)
Resulting cash balance after all actions
Resulting total exposure (% of capital invested)

Aggregate risk:

Total portfolio risk (sum of position risks, if calculable)
Concentration risk (largest position %, largest sector %)
Liquidity risk (% of positions with low liquidity)
A-share specific risks (% with price limit risk, suspension risk, etc.)

Step 5: Generate ranked focus list

Prioritize symbols for deeper review:

Focus list criteria:

High priority (immediate action):
- Buy recommendations with score > 80
- Exit recommendations (stop-loss triggered, major risk flags)
- Reduce recommendations with significant risk flags
Medium priority (review soon):
- Buy recommendations with score 70-79
- Watch recommendations near Buy threshold
- Hold recommendations with moderate risk flags
Low priority (monitor):
- Watch recommendations not near threshold
- Hold recommendations with no risk flags
- Avoid recommendations (no action needed)

Focus list ranking:

Sort by priority (high > medium > low)
Within priority, sort by score (descending)
Within same score, sort by momentum strength
Break further ties by volume confirmation

Focus list output: For each symbol in focus list (top 10-20):

Symbol, name, board
Current action (Buy/Watch/Avoid/Hold/Reduce/Exit)
Score, trend, regime, bias
Priority level (high/medium/low)
Key reason (why this action, why this priority)
Suggested next step (run market-brief, decision-support, etc.)

Step 6: Generate dashboard report

Organize findings into structured dashboard:

Part 1: Dashboard Summary

Universe source and size
Dashboard timestamp
Valid symbols processed
Symbols skipped (by category)
Portfolio context (capital, cash, positions)

Part 2: Action Distribution

Action counts (Buy/Watch/Avoid/Hold/Reduce/Exit)
Action percentages
Trend vs. previous dashboard (if available)
Aggregate capital deployment/reduction
Resulting cash and exposure

Part 3: Aggregate Risk Summary

Total portfolio exposure (% of capital)
Largest position (symbol, %)
Concentration risk (sector, if tracked)
Liquidity risk (% low liquidity positions)
A-share specific risks (price limits, suspensions, etc.)

Part 4: Focus List For each high/medium priority symbol:

Symbol, action, score, regime
Priority level and reason
Suggested next step
Key risk flags (if any)

Part 5: Position Review (if positions held) For each current position:

Symbol, quantity, cost basis
Current price, market value
Unrealized P&L ($ and %)
Action (Hold/Reduce/Exit)
Reason for action
Risk flags (if any)

Part 6: Watchlist Review For each watchlist symbol not held:

Symbol, score, regime
Action (Buy/Watch/Avoid)
Reason for action
Suggested position size (if Buy)
Risk flags (if any)

Part 7: Skipped Symbols

Invalid symbols (count and list)
Suspended symbols (count and list)
Insufficient data (count and list)
Calculation errors (count and list)

Part 8: Dashboard Interpretation

What the dashboard represents (conditional action summary under shared assumptions)
What it does NOT represent (final portfolio instructions, optimized allocation)
Recommended workflow (focus list → deeper analysis → execution planning)
Caveats and limitations (no correlation modeling, no optimization, etc.)

Step 7: Run the local executor

python3 <bundle-root>/decision-dashboard/run.py <symbol ...> [--file PATH] [--dir PATH] [--capital N] [--cash N] [--risk N]

Step 8: Interpret dashboard correctly

When delivering results, maintain proper framing:

Dashboard interpretation:

This is a batch conditional action summary, not a ready-to-trade portfolio
Actions are based on shared assumptions (capital, risk, etc.)
Actions are conditional on deeper verification (thesis, catalyst, execution)
Actions do not account for cross-name correlations or diversification

Assumption transparency:

State which assumptions are user-supplied vs. defaulted
State capital, cash, risk tolerance, position limits
State that assumptions are applied uniformly across batch
State that individual symbols may need different assumptions

Limitations disclosure:

No portfolio optimization: No mean-variance optimization, no efficient frontier
No correlation modeling: Treats each symbol independently
No diversification analysis: Does not assess portfolio diversification
No transaction cost optimization: Does not minimize turnover or costs
No timing optimization: Does not consider optimal entry/exit timing

Recommended workflow:

Review focus list (high priority symbols first)
Run market-brief or analysis on focus list for deeper review
Run decision-support on selected symbols for refined position sizing
Run strategy-design on selected symbols for execution planning
Execute trades via paper-trading or live trading system
Rerun dashboard daily/weekly to monitor and adjust

Step 9: Route focus list to deeper skills

Pass focus list to appropriate next skill:

For high priority Buy recommendations:

market-brief: Quick verification of setup
analysis: Full thesis development
decision-support: Refined position sizing with individual assumptions
strategy-design: Execution planning

For high priority Exit recommendations:

strategy-design: Exit execution planning
paper-trading: Simulated exit to verify execution

For medium priority Watch recommendations:

market-brief: Monitor for trigger conditions
technical-scan: Pattern recognition for entry timing

For position review (Hold/Reduce):

backtest-evaluator: Retrospective evaluation of position
decision-support: Reassess position sizing
strategy-design: Rebalancing execution planning

Output Contract

Minimum local executor output: human-readable text beginning with 决策仪表盘.
Core fields: total symbol count, action distribution, and a ranked focus list.
Partial failure behavior: invalid symbols or malformed per-symbol files are listed under 跳过 if at least one valid result exists.
Caller-facing delivery standard:
- Eight-part structure: Dashboard summary, action distribution, aggregate risk, focus list, position review, watchlist review, skipped symbols, dashboard interpretation
- Universe and assumption transparency: Identify universe source and shared account assumptions (capital, cash, risk, limits)
- Action distribution summary: Counts and percentages for Buy/Watch/Avoid/Hold/Reduce/Exit
- Aggregate exposure and risk: Total deployment, reduction, resulting cash, exposure, concentration
- Focus list prioritization: High/medium/low priority with specific reasons and next steps
- Position review: Current holdings with P&L, action, reason, risk flags
- Conditional framing: Treat action counts as conditional outputs, not final portfolio instructions
- Assumption disclosure: State which assumptions are user-supplied vs. defaulted
- Limitations transparency: State what dashboard does NOT provide (optimization, correlation, diversification)
- Workflow guidance: Recommend focus list → deeper analysis → execution planning
- No ready-to-trade claims: Dashboard requires verification before execution

Failure Handling

Parse and argument errors: non-zero exit with a readable 命令错误 message.
If all candidates fail: readable failure text beginning with 执行失败:.
Mixed candidate quality: valid results still return, and invalid entries are summarized under 跳过.
If assumptions are missing or defaulted, the dashboard should still return but the agent must disclose that limitation.
Invalid symbols: Skip and list under "Invalid symbols" category.
Suspended symbols: Flag and list under "Suspended symbols" category.
Insufficient data: Skip and list under "Insufficient data" category.
Calculation errors: Skip and list under "Calculation errors" category.

Key Rules

Prefer this skill when the user wants breadth first and depth second.
Keep the distinction between symbol lists and data-file directories explicit.
Treat the dashboard as a triage output, not as a substitute for portfolio construction or execution planning.
If the user wants true portfolio risk budgeting, say that this skill does not model cross-name correlations or optimizer logic locally.
Assumption transparency is mandatory. Always state capital, cash, risk tolerance, position limits.
Action distribution must be comprehensive. Count all actions (Buy/Watch/Avoid/Hold/Reduce/Exit).
Aggregate risk must be assessed. Exposure, concentration, liquidity, A-share specific risks.
Focus list must be prioritized. High/medium/low priority with specific reasons.
Position review must include P&L. Unrealized gains/losses for current holdings.
Conditional framing is critical. Actions are conditional on deeper verification, not final instructions.
Limitations must be disclosed. No optimization, no correlation modeling, no diversification analysis.
Workflow guidance must be explicit. Route focus list to appropriate downstream skills.
No cross-name correlation modeling. Each symbol treated independently.

Composition

Often follows watchlist-import.
Commonly feeds market-brief, analysis, decision-support, or strategy-design on shortlisted names.
Shares decision logic with decision-support but applies in batch form.
Can be rerun daily/weekly for ongoing portfolio monitoring.
Should be combined with paper-trading for position tracking and P&L calculation.
Focus list can feed back into market-screen for refined screening.