Before building a strategy, serious quant research starts with understanding the instruments: their current price behaviour, options market structure, fundamental health, and the macro context they sit in. alphabench's Researcher agent gives you direct access to all of this through the chat interface, in the same conversation where you build your strategies.
The Researcher agent
The Researcher is a dedicated sub-agent that handles any question involving market data, fundamentals, news, or macro context. The Planner agent routes you to the Researcher automatically when your question is research-oriented rather than backtest-oriented.
You don't need to explicitly ask for the Researcher. Just ask your question and the system routes it correctly.
1. Live price quotes
"What's the current price of RELIANCE
NSE:RELIANCE, HDFCBANK
NSE:HDFCBANK, and INFY
NSE:INFY? Show me the day's change and volume."
The Researcher calls fetch_market_quotes and returns a table with:
- LTP (last traded price)
- OHLC for the current session
- Volume vs 20-day average volume
- Day's change in both points and percentage
- Market cap (for equity instruments)
You can fetch up to 50 instruments in a single query. This is useful for scanning a watchlist before deciding where to focus research.
"Get quotes for all Nifty Bank INDICES:NIFTY BANK stocks. Which ones are outperforming the index today?"
The Researcher computes relative strength vs BANKNIFTY in the response.
2. Options chain snapshot
For any derivative position research or to understand the market's implied expectations:
"Show me the NIFTY options chain for the current weekly expiry. Focus on strikes within 500 points of spot. Include IV percentile, put-call ratio, and max pain."
The Researcher calls get_options_chain_snapshot and returns:
- Strike-by-strike IV (implied volatility)
- Open interest for calls and puts at each strike
- Put-call OI ratio across the chain
- Max pain strike (the price where maximum options expire worthless)
- IV percentile vs last 30/60/90 days
- The current term structure (near vs monthly IV comparison)
This tells you whether the market is pricing in more or less uncertainty than usual, where the smart money has positioned, and where the index is likely to have gravity.
3. Fundamental deep-dives
For any equity instrument, you can pull a comprehensive set of fundamental metrics:
"Give me a full fundamental breakdown of TITAN: P/E, P/B, ROE, ROCE, revenue growth, operating margins, dividend yield, and debt-to-equity. How does it compare to the consumer discretionary sector average?"
The Researcher calls get_instrument_fundamentals and returns 27+ fields including:
- Valuation: P/E, P/B, EV/EBITDA, market cap, enterprise value
- Profitability: ROE, ROCE, net profit margin, operating margin, EBITDA margin
- Growth: revenue growth (1yr, 3yr), earnings growth, EPS
- Quality: debt-to-equity, interest coverage, current ratio
- Returns: dividend yield, buyback history
- Technical: 52-week high/low, distance from 200-day SMA
For sector comparisons, the Researcher can pull the same metrics for a set of peers and present them side-by-side.
4. Sector and macro context
"What's happening in the banking sector? I've seen HDFCBANK
NSE:ICICIBANK both down significantly this week. Is this broader sector pressure or stock-specific?"
"How is the IT sector positioned given the recent USD/INR move? Which large-cap IT stocks have the highest revenue exposure to the US?"
The Researcher calls financial_market_research which searches recent financial news,
analyst reports, and macro data sources. It synthesizes the relevant context and returns
a structured summary with:
- Key events affecting the sector or instrument
- Analyst consensus and recent rating changes (where available)
- Macro factors: interest rates, currency, commodity prices, FII/DII flows
- What the institutional positioning data suggests
This research feeds directly into your strategy thesis. If the Researcher finds that
HDFCBANK NSE:HDFCBANK's drop is sector-wide due to RBI rate guidance, that's very different from a
stock-specific earnings miss.
5. Connecting research to strategy
The power of the platform is that research and strategy building happen in the same conversation. After the Researcher returns its findings, you can immediately ask the Quant agent to build a strategy based on what you've learned:
"Given the high IV percentile in NIFTY and the sideways price action you described, build an iron condor strategy for next week's expiry."
The Quant agent picks up the research context from the conversation and applies it. No need to repeat the IV data or the market description.
6. Expiry calendar
For options and futures strategies, knowing the exact expiry dates is essential:
"When does the current NIFTY monthly expire? What are the next 3 weekly expiry dates for BANKNIFTY? Are any of these around major events (RBI policy, budget day)?"
The Researcher calls get_expiry_calendar and returns:
- Upcoming weekly and monthly expiry dates for NIFTY and BANKNIFTY
- Any known macro events (budget, RBI MPC, quarterly earnings seasons) near those dates
Expiry weeks with known macro events typically have elevated IV and unpredictable intraday behaviour. Good adaptive strategies avoid holding positions through them or switch to delta-neutral modes around those dates.
7. Charting for visual context
"Show me a 6-month daily chart of RELIANCE
The Researcher calls fetch_instrument_chart and returns a visual candlestick chart
rendered inline in the chat. Use this to visually confirm what the data is telling you
before committing to a thesis.
8. Multi-instrument comparison
For relative value or pairs research:
"Compare the last 6 months of price performance between HDFCBANK
The Researcher pulls chart data and fundamentals for both, computes relative performance, and returns a comparison table. This is the starting point for a pairs trading strategy.
See Pairs Trading with Z-Score for the full workflow.