| name | news-trading |
| description | Plan and evaluate trades around scheduled economic or corporate releases. Use when the user needs primary-source event data, standardized surprise measurement, scenario triggers, and execution-risk controls. |
| license | Apache-2.0 |
| metadata | {"author":"ske-labs","version":"4.0"} |
News Trading Strategy
Trade volatility from scheduled economic events using deviation-from-consensus scoring.
Setup Conditions
Event Inputs
Use a live official calendar, release timestamp and revision policy. Record every economically relevant component: for example, headline and core inflation; payrolls, unemployment, wages, and revisions; or a policy decision, statement, projections, and press conference. Do not attach a universal price move to an event name.
Deviation-from-Consensus Scoring
Standardized surprise = (Actual - consensus) / historical standard deviation of surprises
Use a rolling, vintage-consistent surprise history and state the consensus provider and cutoff time. If history is insufficient, show the raw surprise and estimate range without inventing a score. Direction also depends on priced expectations, revisions, other components, and policy regime.
Workflow
1. Check Economic Calendar
get_economics_calendar(from_date=<start>, to_date=<end>, impact="high")
Identify upcoming high-impact events. Note dates, times, affected markets.
2. Research Consensus
get_financial_news(topic="<event> forecast consensus <month> <year>", max_results=15)
Find the current consensus, estimate range, cutoff timestamp, and prior value. Confirm the actual and revisions from the official release, not a headline or social post.
3. Plan Scenarios
Pre-plan multiple component combinations and observable price triggers. A headline beat need not imply one direction when revisions, core measures, guidance, or policy expectations conflict.
4. Post-Release Entry
Enter only when the selected execution gate is met: spread below a tested limit, quotes stable enough to size, and a closed-bar or order-book trigger. Calibrate any waiting period in bars for the specific market. Use a volatility-based stop with position size reduced to keep risk fixed; include gap, halt, rejection, and slippage scenarios.
5. Event Clustering
When releases overlap, attribute the move cautiously and wait until all scheduled components are available. If they conflict or the event timestamp/data vintage is uncertain, return no trade.
6. Report to Orchestrator
Event details, consensus vs actual, deviation score, recommended action, conviction level.
Evidence and Validation
- Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
- Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
- Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
- Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
- Research basis: Federal Reserve research finds more volatility jumps on announcement days and sensitivity to surprise and uncertainty.
Key Rules
- Never use unreleased, embargoed, or suspected material nonpublic information.
- Do not hold through an event without an explicit event-risk mandate and gap-loss scenario.
- Size from the stress loss and executable stop distance, not a fixed percentage reduction.
- Skip conflicting clustered releases or failed liquidity gates.
- Model spread widening, slippage, rejected orders, halts, revisions, and timestamp latency.
Related Skills
- breakout-trading -- post-news consolidation breakouts follow breakout principles
- gap-trading -- news events often produce gap opens
