Best Research Platform?

maybe not relevant for L/S but what are thoughts on the broker/sell side research platforms? I've used citi velo, JPM MM, and marquee/GS Quant for rates/quant stuff and imo they provided a better dev/analyst experience than bbg

BBG (mkt data) + VA (consensus) + MaidenCentury (alt data) = trifecta

Bloomberg for most things - can use MODL for consensus and then document search for street research 

Bamsec for filing/transcript search

Tegus / GLG for expert calls and transcripts

I didnt love AS when I tried it- mainly because I get all its functionality from the other stuff. They bought Tegus/BamSEC but it’s like $7k more to bundle it all vs leaving as-is. 

AlphaSense is on a good product roadmap with the acquisition of Tegus. Good coverage of tech market with the database of call transcripts.

I’ve been using edmundsec vs bamsec the past year ish and I think I like it more. It’s a little more cumbersome for proxies but the table downloads across periods is way better than what bamsec gives you. The AI transcript summaries are decent too.

Would this be beneficial to you? Built a financial research platform, looking for honest feedback

I've been building something and I want to know if it's useful to people who actually work in finance. I'm not selling anything in this post — I'm looking for honest feedback from professionals who know what they need.

The historical data:

I've spent months acquiring and organizing 100+ years of financial data into a single queryable platform:

• Kenneth French Data Library: 343 datasets, 85.5 million rows, 1926 to present. The complete library — not individual CSVs. One queryable database.
• SEC EDGAR XBRL financials: 60 million rows of raw company fundamentals. Deeper than CapIQ's standardized version because you get the raw filings.
• FRED macro data, FINRA short interest (3.8M rows), SEC 13F institutional holdings (134K rows), CFTC COT positioning (167K rows), SEC fail-to-deliver (1.8M rows)
• 54 databases total, 568 million rows, 100+ years of history. All locally queryable. No API limits. No per-query costs.

Would having 100 years of Kenneth French factor data in one database — queryable from Python or a web interface — be useful to anyone here? Would it changed your research and how? 

The tools:

On top of that data, I built 76 ML-powered analytical tools. Here are the ones I think would matter most to this community:

For quant researchers:

• Walk-forward validation with combinatorial purged cross-validation (Lopez de Prado's method from "Advances in Financial Machine Learning"). PBO (probability of backtest overfitting). Deflated Sharpe ratio. Implemented natively — not as a library call, as a standalone tool with 100 years of factor data built in.
• Signal half-life computer: measures how long your signal works before it decays. IC at 1d/5d/10d/20d/60d/90d/180d lags. Crowding score (0-100). Recommended rebalance frequency.
• Crash Oracle: Bayesian 60-day crash probability using 7-signal ensemble. No other platform has this.
• Residual decomposition: what explains the 30% of returns your factor model doesn't capture?

For hedge fund PMs:

• Short-squeeze risk alert: FINRA short interest + borrow rate + fail-to-deliver + utilization → 0-100 squeeze score per name
• Kelly position sizing with crisis path Monte Carlo: adjusts position size for crisis events that multiply losses
• Options Greeks + IV surface: Black-Scholes Greeks via QuantLib, implied vol surface, skew metrics
• CDS spreads: FRED iTraxx/CDX indices + name-level estimate

For portfolio managers:

• Tracking error calculator: ex-ante + ex-post, factor decomposition, rolling TE, mandate compliance check. "Am I within my 3.5% limit?" in one call.
• Multi-period Brinson attribution: allocation/selection/interaction across multiple periods with linking algorithm
• Client report generator: auto-generate performance reports with attribution, risk metrics. 6-8 hours/week → 5 minutes.
• Tax-aware rebalancing: compute tax impact of every trade, suggest tax-loss harvesting

For IB analysts:

• Comp set builder: save peer groups, compute all multiples (P/E, EV/EBITDA, EV/Sales, P/B), flag outliers, export formatted Excel. The 90-minute comp rebuild → 15 minutes.
• DCF / LBO / accretion-dilution models: three model types with sensitivity tables
• M&A precedent transactions database: 25+ deals with multiples, premiums, advisor roles
• Excel integration: generates xlwings code that pulls live data into your models (BDP/BDH replacement)

For CROs / risk professionals:

• CVA/DVA/FVA engine: counterparty credit risk adjustments via QuantLib. Rating-based default curves.
• Multi-asset stress test: 9 historical scenarios (2008, 2020, 1987, 1998, 2010, 2022) + hypothetical (oil shock, dollar crisis, cyber attack)
• Beta inflation factor: crisis beta / bull beta ratio. "Your beta inflates 1.36x in bear markets — use 1.35 for risk management, not 1.08."
• Systemic anomaly timing: correlation distribution monitoring. Stress score 0-100. Early warning when correlations spike.

For wealth managers:

• Estate planning calculator: GRAT, dynasty trust, ILIT, gifting strategy. Shows planning saves $93M on $50M estate.
• Client vulnerability ranking: 6-factor behavioral scoring. Which clients are most likely to panic sell?
• Sequence-of-returns analysis: same average return produces $2.57M (best case) or $0 (worst case) depending on order. Most powerful client communication tool I've seen.
• Today vs 2008 comparison: 8 metrics compared. Client talking points for anxious clients.

The pricing thoughts, is this fair?:   $50-100/month flat. No per-query costs. No API limits. No $24,000/year. 

The honest limitations:

• No live trading. This is a research platform, not an execution platform. If you need to place trades, this is not your tool.
• Real-time tick data is not yet integrated (working on it). Live quotes use free sources (Yahoo Finance, Finnhub, Binance, TradingView) which can be upgraded to paid feeds.
• No community yet. I'm posting here because I want feedback and thoughts, and I wanted that to start with people who know what good looks like.

What I'm asking: Would this be beneficial to you? Would some of you financial folks be interested in what I have built? I'm looking for honest feedback — what's useful, what's missing, what answers a pain point of yours, what would make you consider using it?

If curious, I’m not young, approaching retirement myself. This all started when I attempted to build my own institutional grade RMS or Retirement Management System. It evolved and I realized it’s something unique and maybe worth sharing. Thus I ask for your feedback and thoughts and honestly doing ‘this’ wasn’t easy, little intimidating. Hopefully I articulated the above well. 

Thank you for your time.

Is visible alpha really that necessary when everything is all about the whisper number now, not really about sell side consensus anymore?

Bit of an old thread but we've built a pretty good alternative to FactSet/Capital IQ. We're ex M&A bankers.

Multiples .vc - it has both public and private comps (M&A and funding rounds). Data licensed by FactSet and Morningstar, so there isn't any loss in quality if that's a concern.

For public comps we have both historical and estimates data (3 years forward). 

I wouldn't say it's a full workstation replacement (we don't have all bells and whistles like contact data, patent data etc.) - but for valuation/comps purposes it's pretty good, and we're fraction of the price vs. big guys. 

No long term contracts too, it's a self-serve SaaS with free trial.