Research Tasks
A research task tells the agent what kind of market pattern to test.
Research tasks are the starting point for a Quandora run.
They give your AI agent a focused market behavior to investigate, instead of asking it to "find alpha" from a blank prompt. A task turns a broad trading question into a structured research job with a goal, allowed data fields, hints, and an evaluation horizon.
Why Research Tasks Matter
AI agents are good at generating ideas, but finance research needs more than ideas. A useful trading idea needs a clear question, usable data, testable logic, and a way to decide whether the result is strong or weak.
Research tasks give the agent those rails.
Instead of starting with:
Find me a good trading strategy.Quandora starts with a narrower job:
Find a testable factor that detects liquidity fragility before volatility expands.That difference matters. A narrow task helps the agent produce something Quandora can evaluate: a factor artifact, a formula, and a result card.
Current Research Task Grouping
Quandora research tasks are grouped by the kind of market behavior they ask the agent to investigate.
The current canonical categories are Technical, Microstructure, Volatility, Imbalance, Order Flow, Auction, Momentum, Volume, and Liquidity. A custom idea that does not match one of these families can use the Other fallback. The task response returned in the current session is the source of truth.
Technical Price Action & Pattern Structure
This task asks whether recurring price-action or multi-bar structure contains a testable cross-sectional signal.
It looks for patterns in returns, ranges, closes, breakouts, reversals, and volatility structure while avoiding a purely visual or discretionary chart reading.
Plain-English question:
Market Microstructure And Liquidity Fragility
This task asks whether a market is becoming fragile.
It looks for signs that liquidity is weakening, price is becoming easier to move, or liquidation cascades may create unstable conditions. The agent may study volume, taker flow, open interest, funding, liquidations, and premium data to create a testable liquidity-fragility factor.
Plain-English question:
Volatility Regime And Risk Compensation
This task asks whether the market is moving into a different volatility regime.
It looks for panic, squeeze setups, volatility expansion, and changing risk conditions. The agent tries to detect whether the market is becoming calmer, more explosive, or more unstable.
Plain-English question:
Order Imbalance And Persistent Pressure
This task asks whether there is persistent buy or sell pressure.
It looks for accumulation, distribution, trend continuation, and one-sided market pressure. The agent studies whether buyers or sellers are consistently dominating over time.
Plain-English question:
Aggressive Order Flow And Informed Trading
This task asks whether aggressive traders are pushing the market in one direction.
It looks at taker flow, aggressive volume, short-term drift, and possible signs of informed capital. The agent studies whether fast market participants are leaving a useful footprint.
Plain-English question:
Funding, Premium And Positioning Crowding
This task asks whether leverage is becoming crowded or unstable.
It looks for funding reversals, premium deviations, crowded positioning, and trend exhaustion. The agent studies whether too many traders are leaning the same way.
Plain-English question:
Price Momentum And Trend Quality
This task asks whether a trend is clean enough to trust.
It looks for trend-following factors with quality filters such as smoothness, volume confirmation, and flow support. The agent studies whether price movement is persistent or just noisy.
Plain-English question:
Volume Shock And Trend Confirmation
This task asks whether unusual volume confirms a price move.
It looks for abnormal volume, information arrival, and whether market activity supports or rejects a move. The agent studies whether a price move has real participation behind it.
Plain-English question:
Liquidity Premium And Trading Cost
This task asks whether a signal is still useful after trading costs.
It looks for slippage risk, liquidity collapse, trading cost, and impact-adjusted liquidity. The agent studies whether an idea is practical after accounting for the cost of actually trading it.
Plain-English question:
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