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AI Tools for Quantitative Analysts
AI tools that help quantitative analysts research trading signals, pull market data, model risk metrics, and access academic finance literature.
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Ticker5y return12 1 mo momentum
AAPL+189%+24.1%
MSFT+213%+28.4%
NVDA+1,840%+142%
SPY+94%+18.7%
1,260 trading days · dividend-adjusted · split-adjusted
Market data acquisition for factor models
Pull historical price, volume, and return data for large stock universes to construct and backtest factor models. Get clean adjusted price series for any equity, ETF, or index across multiple years.
I need 5 years of daily adjusted closing prices and volume for all S&P 500 components for a momentum-reversal study. Can you pull prices, 52-week high, 12-1 month momentum, and 1-month reversal for a sample of 20 stocks?
Pulled 5-year daily data for 20 S&P 500 stocks. Built the factor signals: 12-1 month momentum (month -12 to -1 return), 1-month reversal (prior month return), distance from 52-week high. Sample statistics: momentum decile spread (top - bottom) = 18.3% annualized, reversal decile spread = -4.1% annualized, consistent with Jegadeesh-Titman findings. Data ready for full universe extension.
ToolRouter get_history
Ticker12 1 mo momentum1 mo reversal
AAPL+24.1%-1.8%
MSFT+28.4%-0.9%
JPM+18.2%-2.4%
XOM-4.1%+1.2%
20 stocks · 5Y daily · momentum decile spread +18.3% annualized
ToolRouter get_rates
10Y Yield (%)
VIX
Academic finance literature research
Search peer-reviewed journals for factor model construction papers, risk model methodologies, and market microstructure research. Access citations, abstracts, and full paper details for the academic foundations of your models.
Find the most cited papers on equity factor model construction — specifically the Fama-French 3-factor and 5-factor papers, and any papers published after 2015 that extend or challenge the 5-factor model.
Found 47 papers. Top results: Fama & French (1993) — 3 factors, 31,000+ citations. Fama & French (2015) — 5-factor model, 8,200+ citations. Notable post-2015 challenges: Hou, Xue, Zhang (2015) q-factor model (4,100 citations); Stambaugh & Yuan (2017) mispricing factors; Harvey, Liu, Zhu (2016) "...and the cross-section of expected returns" (critiques factor zoo). All papers include DOI links and citation counts.
ToolRouter search_papers
AuthorsYearTitle
Fama & French19933-factor model (mkt, SMB, HML)
Fama & French20155-factor model (adds RMW, CMA)
Hou, Xue, Zhang2015q-factor model alternative
Harvey, Liu, Zhu2016"...and the cross-section" — critiques factor zoo
4 of 47 papers · DOI links included
Risk-adjusted performance metrics calculation
Calculate Sharpe, Sortino, Calmar, and Information Ratios for strategy evaluation. Model maximum drawdown, drawdown duration, and underwater curves for a portfolio or backtest return series.
Calculate full performance attribution for a 3-year strategy: annualized return 14.2%, benchmark (SPY) return 11.8%, strategy volatility 16.8%, benchmark volatility 15.2%, correlation 0.74, risk-free rate 4.5%. Give me all standard performance metrics.
Alpha (Jensen's): +3.1% annualized. Beta: 0.82 (lower market exposure). Sharpe ratio: 0.58 (strategy) vs 0.48 (benchmark). Sortino ratio: 0.84. Information ratio: 0.47. Tracking error: 10.1%. Treynor ratio: 11.8%. M-squared: 11.0% (risk-adjusted return on equivalent-risk basis). Maximum drawdown: -22.4%. Calmar ratio: 0.63.
ToolRouter calculate_metrics
Jensen's Alpha
+3.1% annualized
Sharpe Ratio
0.58 (strategy) vs 0.48 (benchmark SPY)
Beta
0.82 — below-market exposure
Information Ratio
0.47 · Tracking error: 10.1%
Max Drawdown
-22.4% · Calmar: 0.63
ToolRouter create_chart
Strategy Return (%)
SPY Return (%)
Macro regime and factor research
Research how macroeconomic regimes (rising rates, high inflation, recession) affect factor performance. Build the macro regime framework that feeds your factor tilting or risk-on/risk-off signals.
Research factor performance during different interest rate regimes. Specifically: which equity factors (value, momentum, quality, low vol, size) historically outperform when the 10-year yield is rising vs falling?
Synthesized from AQR Capital, MSCI factor papers, and 12 academic studies. Rising rate environments: Value +3.2% annualized alpha vs market, Quality +2.1%, Momentum -0.8% (tends to struggle in sharp rate reversals). Falling rates: Momentum +4.1%, Low Vol +2.8%, Size flat. Key paper: Leippold, Ziegler (2022) "Factor Zoo" shows rate regime explains 35% of cross-factor return variation. Actionable: current environment (rates declining) favors Momentum and Low Vol tilts.
ToolRouter research
Rising rates: Value
+3.2% annualized alpha — outperforms in rate rising environments
Rising rates: Quality
+2.1% annualized alpha — consistent outperformance
Rising rates: Momentum
-0.8% — struggles in sharp rate reversal periods
Falling rates: Momentum
+4.1% — strongest factor in current declining rate regime
Falling rates: Low Vol
+2.8% — currently favorable given rate trajectory
Options and derivatives pricing research
Research options pricing models, volatility surface dynamics, and derivatives hedging strategies. Access academic papers on VIX forecasting, implied volatility surfaces, and the volatility risk premium.
I'm researching the volatility risk premium strategy — selling short-dated options to harvest implied vs realized vol spread. Find academic evidence on the magnitude and persistence of this premium in S&P 500 options.
Found 23 relevant papers. Key findings: Bakshi & Kapadia (2003) first quantified the negative volatility risk premium — implied vol exceeds realized by 3-5 volatility points on average. Carr & Wu (2009) showed the premium is largest in 1-month options. More recent: Eraker (2004), Santa-Clara & Yan (2010) — premium is compensation for jump risk, not just model error. Strategy Sharpe ratios reported in literature: 0.8-1.4 before transaction costs, 0.4-0.8 after. Tail risk (2008, 2020): -50% to -80% drawdowns possible — cannot be ignored.
ToolRouter get_rates
Implied Vol (VIX)
Realized Vol
ToolRouter search_papers
AuthorsYearFinding
Bakshi & Kapadia2003Implied exceeds realized by 3–5 vol points on average
Carr & Wu2009Premium largest in 1-month S&P options
Santa-Clara & Yan2010Premium compensates for jump risk, not model error
3 of 23 papers · 2008/2020 drawdown risk: -50% to -80% noted in all studies
Ready-to-use prompts
Historical price data for factor construction
Pull daily adjusted closing prices and volume for these 10 stocks over the last 3 years: AAPL, MSFT, NVDA, GOOGL, META, AMZN, JPM, BRK.B, UNH, and XOM. Also pull SPY and QQQ for benchmark comparison. I need this for a momentum and mean-reversion factor study.
Search academic finance papers
Find peer-reviewed papers on the low volatility anomaly in equity markets — specifically papers by Baker, Bradley, and Wurgler. Also find any papers since 2018 that examine whether the anomaly has been arbitraged away since it became widely known.
Sharpe and drawdown metrics
Calculate performance statistics for a strategy with these annual returns: 2019: +18.2%, 2020: -12.4%, 2021: +24.1%, 2022: -8.7%, 2023: +15.3%, 2024: +11.8%. Risk-free rate: 4.5%. Calculate: annualized return, annualized volatility, Sharpe ratio, Sortino ratio, maximum drawdown, and Calmar ratio.
Macro factor regime research
Research the historical performance of value vs growth factors during periods of high inflation (CPI above 4%). Include data from the 1970s, 2021–2022 inflation episode, and any academic studies on inflation regime effects on equity factors.
Risk-free rate and VIX data series
Pull the daily 3-month US T-bill rate, 10-year Treasury yield, and CBOE VIX index for each trading day from January 2020 to present. I use these as inputs to my risk model for estimating the equity risk premium and fear indicator.
Correlation matrix for risk model
Calculate the 12-month rolling correlation between SPY, AGG, GLD, DXY, USO, VNQ, and EEM. Show the correlation matrix for both current period and 5-year average, and flag any pairs where correlation has shifted more than 0.20.
Volatility surface research
Research how implied volatility smiles and skews behave around earnings announcements for large-cap tech stocks. Find academic evidence on the implied vs realized volatility spread for single-stock options versus index options, and which is more persistently positive.
WACC and cost of equity calculation
Calculate WACC for a stock with: beta 1.35, current 10-year Treasury yield 4.38%, equity risk premium 5.5%, $15B market cap, $4B in long-term debt at 5.2% coupon, 25% marginal tax rate. Show each component.
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New factor signal research and validation
Before building a new factor into the model, research the academic foundation, pull empirical data, and validate the signal holds in recent out-of-sample data.
Strategy performance attribution
After each quarter, decompose strategy returns into factor exposures, alpha, and transaction cost drag to understand what is actually driving performance.
Macro regime change research
When macro conditions shift (Fed pivot, inflation surge, recession), research how this regime historically affects your factor exposures and whether model tilts are warranted.
Frequently Asked Questions
What is the best source for clean historical equity data for backtesting?
The Stock Market tool provides adjusted historical prices and volume data for US and international equities, ETFs, and indices. For academic-grade backtesting, data quality considerations include survivorship bias (many databases exclude delisted stocks), point-in-time fundamental data (versus as-reported), and dividend-adjusted vs. unadjusted prices. Use adjusted close prices for return calculations.
How do I avoid data snooping bias when developing a factor model?
The most common pitfall: testing many signals on the same dataset and selecting the one that worked best — this inflates apparent Sharpe ratios by 2–3x. Best practices: (1) reserve a true out-of-sample test period before you start, (2) require academic pre-existence of the signal (search Academic Research tool for papers published before your test period), (3) apply Harvey, Liu, Zhu (2016) multiple testing corrections — a t-stat above 3.0 is now required to claim statistical significance.
What is the Information Ratio and how does it differ from the Sharpe Ratio?
Sharpe Ratio = (strategy return - risk-free rate) / strategy volatility — measures total risk-adjusted return. Information Ratio = (strategy return - benchmark return) / tracking error — measures return above benchmark per unit of active risk. For a long-only fund benchmarked to the S&P 500, IR is more relevant than Sharpe. A top-quartile equity fund has an IR around 0.5–0.75. An IR above 1.0 is rare and impressive.
How do I handle the look-ahead bias in factor construction?
Look-ahead bias occurs when your factor uses information not available at the time of the trading decision. Common examples: using annual earnings reported in February to construct a January factor signal, or using the quarterly close price in the same quarter you are trading. Fix: always use data lagged by at least one reporting period. For fundamental data, use a 3-month lag from fiscal year end to ensure publication.
What is the Fama-French 5-factor model and when should I use it vs the 3-factor model?
The 3-factor model (1993) uses market, size (SMB), and value (HML) factors. The 5-factor model (2015) adds profitability (RMW) and investment (CMA) factors. The 5-factor model explains more cross-sectional return variation but makes HML redundant in many datasets. Use the 5-factor model for performance attribution of diversified equity funds. For momentum strategies, neither model fully captures momentum — add a momentum (UMD/WML) factor as a 6th factor.
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