Security Score Tables
Cross-sectional ranks, percentiles, Z-scores, and composite measures across technical, fundamental, risk, and market-structure families.
Systematic equity research · institutional derived data
Echo
The data layer for AI-native investment research: structured weekly, monthly, and point-in-time equity analytics for professional researchers.
767Unified securities
768,724Weekly observations
176,646Monthly observations
706Structured metric choices
Capabilities
Calculated once. Curated continuously. Delivered wholesale.
Echo converts licensed source data into consistent, point-in-time, backtest-ready histories so investment firms can spend more time analyzing security histories and less time rebuilding infrastructure.
Historical Signal Archives
Multi-window calculated series preserved through time for security-level analysis and institutional backtesting.
Metric History Pages
Every historically stored snapshot field opens a dedicated security page with one canonical chart and the latest dated observations.
Stock Screening
Screen the current universe using any available technical, fundamental, ranking, or composite field.
Research Curation
Security-master controls, coverage audits, formula versioning, missing-data rules, and repeatable production validation.
Institutional Delivery
Bulk Parquet and CSV files, APIs, SQL-ready tables, and bespoke calculation services for professional clients.
Data infrastructure
The Technical Vault
A survivorship-aware calculated-data archive designed to make security research and backtesting immediately usable.
MomentumReturn persistence across multiple windows
Price dislocationHistorical and cross-sectional Z-scores
Volume & liquidityDollar-volume shocks and relative ranks
Risk & distributionBeta, downside deviation, skewness, and residual risk
Fundamental qualityProfitability, value, growth, safety, and sector-relative measures
Signals about signalsRanks, persistence, relative values, and composite outputs
The engine
How Echo works
A repeatable production process that separates source data, calculated histories, and client-facing research outputs.
Curate
Reconcile securities, dates, classifications, and historical coverage.
Calculate
Apply standardized formulas across securities, dates, and windows.
Normalize
Create ranks, percentiles, Z-scores, and peer-relative values.
Validate
Audit uniqueness, missingness, coverage, and point-in-time integrity.
Deliver
Publish research pages, charts, screeners, files, and APIs.
Prototype library
Explore real Echo data, not a static mockup.
The local research portal is backed by the unified Echo security master, separate weekly and monthly histories, point-in-time fundamentals, structured metric registry, and validation reports.
What is Echo?
A repeatable engine for systematic equity research.
Echo converts market data, factor signals, model portfolios, and company information into structured research outputs for professional users.
Product overview
Research infrastructure, not a black-box stock picker.
Echo is an AI-assisted systematic equity research and analytics platform. It organizes technical, fundamental, risk, volume, and market-structure information into comparable security-level histories and cross-sectional rankings.
Rather than replacing investment judgment, Echo provides a consistent research layer that can support idea generation, portfolio research, client discussion, market commentary, and institutional data workflows.
Product boundaryEcho is not designed to provide personalized investment advice, manage client capital, or guarantee investment performance.
Core outputs
What Echo produces
The platform translates a large calculated-data library into research products that can be viewed in the portal, archived through time, or delivered to institutional systems.
Security Score Tables
Ranks securities across momentum, value, quality, risk, volume, and market structure using cross-sectional percentiles, Z-scores, and ordinal ranks.
Model Baskets
Converts factor rankings into model portfolios and research baskets for systematic evaluation and monitoring.
Security Research Pages
Combines current snapshots, fundamentals, dedicated metric histories, recent observations, and cross-sectional context.
Factor & Risk Diagnostics
Evaluates turnover, drawdown, volatility, beta, correlation, sector exposure, and regime behavior.
Research Archive
Supports date-stamped research outputs with score tables, model versions, source metadata, and disclosures.
Enterprise Data Delivery
Provides calculated rankings and factor datasets through bulk files, APIs, SQL-ready tables, and bespoke research services.
How Echo works
From a broad security universe to repeatable research.
Echo separates data preparation, security ranking, portfolio research, validation, and publication into a transparent five-stage process.
Equity Universe
Begins with a broad, historically controlled universe of equities and ETFs.
Cross-Sectional Ranking
Standardizes technical, fundamental, and risk variables into comparable analytics, percentiles, and Z-scores.
Factor Sleeves & Models
Combines independent rankings into composite scores, research sleeves, and model baskets.
Simulation & Validation
Evaluates behavior through historical simulation, transaction costs, and out-of-sample testing.
Research Delivery
Publishes security pages, rankings, model outputs, and—after review—AI-assisted research notes.
Designed for
Professional research users
Brokerages, RIAs, family offices, asset managers, hedge funds, analysts, and research platforms can use Echo to investigate securities and organize systematic evidence.
Common objective
Transform information into structured decisions
Echo does not eliminate judgment. It embeds judgment in data selection, signal design, portfolio architecture, risk controls, and the interpretation of repeatable research outputs.
Working prototype
Explore Echo as software, not a static presentation.
Search a security, inspect its current metrics, open a dedicated history page, or rank the universe through the cross-sectional screener.
Why Echo?
Markets rarely communicate through a single signal.
Echo is designed to identify when related information recurs across different metrics, time horizons, and research methods.
The name
Signals become more meaningful when they return from several directions.
Financial information appears through price behavior, momentum, trading volume, company fundamentals, risk characteristics, macroeconomic conditions, and relationships with other assets. Each measure captures only part of the picture.
A change first observed in price may also appear in volume, relative rankings, downside behavior, company information, or cross-asset relationships. When several independent observations point in a similar direction, the underlying signal becomes clearer?like an echo returning from multiple surfaces.
Signal architecture
From individual observations to combined research signals.
Echo preserves each metric separately before examining how related measurements reinforce, offset, or condition one another.
Different wavelengths
Weekly market behavior, monthly trends, filing-aware fundamentals, and slower macro relationships operate at different speeds and serve different research purposes.
Comparable rankings
Unlike variables are converted into percentiles, Z-scores, and ordinal ranks so momentum, quality, valuation, risk, and volume can be evaluated on a common scale.
Joint confirmation
Multiplicative and gated models can require several favorable characteristics at once, rather than allowing one exceptional metric to overwhelm weakness elsewhere.
Alternative geometry
Additive, multiplicative, threshold, convex, hierarchical, and ensemble structures test different ideas about how information should interact.
Cross-asset echoes
Security regressions help reveal relationships with rates, credit, commodities, currencies, equity styles, and other market exposures.
Current context
AI-assisted company-news summaries add a qualitative publication layer alongside Echo?s structured technical and fundamental records.
Research applications
Portfolio construction is one test of whether the echoes contain information.
Echo?s research found that portfolio geometry matters. Different levels of concentration, replacement, turnover control, weighting, and model diversification produced different outcomes even when they began with related underlying signals.
Some combinations and portfolio structures performed better than others in historical research. The broader conclusion, however, is not that one portfolio defines Echo. It is that the ranking archive can support multiple research applications and allows users to apply their own mandates, constraints, and investment judgment.
Research boundary
Historical and simulated portfolio studies are validation applications. They do not represent managed performance, guarantee future results, or define the commercial limit of the platform.
What Echo listens for
Repeated information across independent dimensions
The objective is not to force every metric into agreement. It is to identify when several distinct observations provide useful confirmation, contradiction, or context.
What Echo preserves
The signal and the evidence behind it
Each output remains connected to its security, date, underlying observations, ranking population, calculation method, source record, and publication version.
Explore the echoes
Move from the concept to the underlying security data.
Inspect technicals, fundamentals, graphs, regressions, rankings, and current company news within the operating Echo prototype.
Data & Methodology
A point-in-time, cross-sectional research framework.
Echo is built around historically consistent data, transparent factor definitions, cross-sectional normalization, implementation-aware testing, and repeatable publication controls.
Research thesis
Identify information-driven repricing conditions.
Echo searches for recurring traces in price behavior, volume, fundamentals, risk, and broader market conditions, then evaluates those observations through a disciplined portfolio-research process.
Information-Driven Selection
Identifies securities undergoing potential repricing using momentum, volume, valuation, quality, and risk characteristics.
Cross-Sectional Ranking
Converts variables into percentiles, Z-scores, and ranks that can be compared consistently across securities and market regimes.
Signals + Fundamental Filters
Combines price-based information with valuation, profitability, balance-sheet, and quality measures.
Ensemble Construction
Combines independent factor sleeves to reduce reliance on a single signal, window, or market environment.
Implementation-Aware Design
Incorporates turnover, transaction costs, liquidity constraints, position limits, and weight smoothing into research.
Dynamic Risk Governance
Evaluates volatility, cross-asset conditions, beta, drawdown, and defensive overlays as part of exposure management.
Data infrastructure
The Technical Vault
Echo’s target architecture is a survivorship-aware factor archive with weekly and monthly calculated histories, point-in-time fundamentals, and cross-sectional outputs that can be traced back to their source fields and formula versions.
The current prototype demonstrates the architecture using public and prototype sources. Commercial production data remains subject to vendor licensing and data-rights review.
Momentum & trendReturn persistence, moving averages, and relative-strength measures
Price dislocationHistorical deviations, mean-reversion measures, and Z-scores
Volume & liquidityTrading activity, dollar volume, shocks, and cross-sectional ranks
Volatility & riskBeta, downside deviation, residual risk, skewness, and drawdown
Fundamental value & qualityValuation, profitability, growth, leverage, safety, and efficiency
Market structureBenchmark relationships, sector context, macro sensitivity, and regime indicators
Canonical data layers
Separate frequencies. Unified security identity.
Echo preserves each dataset at its natural frequency and composes the client view at query time rather than copying every value onto every date.
Technical & Ranking History
Momentum, volume, risk, market relationships, ranks, percentiles, Z-scores, and derived outputs.
- Security-date observations
- Multiple rolling windows
- Cross-sectional comparisons
- Dedicated history pages
Longer-Horizon Analytics
Lower-frequency technical and risk measures stored independently from the weekly archive.
- Monthly observations
- Long-duration windows
- Portfolio research inputs
- No frequency contamination
Fundamental History
Filing-aware company information and derived ratios aligned to the dates on which information became available.
- Financial statement fields
- Value and quality ratios
- Growth and safety measures
- Historical as-of dates
Research pipeline
From source data to published output
The operating principle is simple: calculate once under documented rules, validate the result, and reuse the canonical history across security pages, screeners, portfolio research, and institutional delivery.
Source
Ingest prices, volume, fundamentals, classifications, benchmarks, and macro series.
Normalize
Reconcile security identities, dates, units, missing data, and point-in-time eligibility.
Calculate
Apply documented formulas across securities, dates, frequencies, windows, and benchmarks.
Validate
Test uniqueness, coverage, missingness, outliers, chronology, and reproducibility.
Publish
Promote validated snapshots, histories, ranks, charts, files, and research outputs.
Validation framework
Designed to reduce avoidable research bias.
Historical simulation remains hypothetical. Echo’s methodology emphasizes controls that improve interpretability without claiming to eliminate model risk.
Point-in-Time Discipline
Historical values should reflect information that was available on each observation date.
Out-of-Sample Testing
Research is selected in training periods and evaluated separately in later test periods.
Implementation Controls
Transaction costs, turnover, liquidity, position limits, and exposure constraints are included where applicable.
Transparent Limitations
Backtests may still contain model sensitivity, universe bias, source-data limitations, and assumptions that differ from live implementation.
Systematic research
Consistency across a broad universe
Rules-based evaluation, cross-sectional ranking, signal aggregation, diversified sleeves, and systematic risk controls create a repeatable process.
Fundamental context
Explainability at the company level
Financial statements, valuation, profitability, growth, leverage, and qualitative review remain essential for interpreting the statistical output.
Inspect the implementation
See the metric definitions, archive coverage, and system architecture.
The research portal exposes the current metric library, validation coverage, and a visual explanation of Echo’s separated data layers.
Institutional data
Calculated research infrastructure for professional investors.
Echo’s datasets and delivery channels are organized separately: clients choose the coverage they need and the format that fits their research stack.
Datasets
Coverage
Equity data is the initial core. Macro and fixed-income layers can be added as the archive and commercial licensing expand.
Equity & ETF Research Database
Prices, returns, technical metrics, point-in-time fundamentals, cross-sectional rankings, composite scores, and security-level histories.
- 767 securities: 668 equities plus 99 current ETFs/ETPs
- Weekly and monthly history from 2000
- Point-in-time fundamental history
- 706 structured metric choices
Macro Data
Rates, inflation, yield curves, credit spreads, currencies, commodities, and cross-asset market indicators.
- Time-series archive
- Security sensitivities
- Rolling regressions
- Regime diagnostics
Fixed Income
A focused corporate-credit layer designed to complement security-level equity research.
- Issuer and instrument mapping
- Yield and spread measures
- Duration and convexity
- Equity-credit context
Delivery
Use Echo in the format your team already works in.
Bulk Downloads
Build a CSV from selected securities, historical metrics, and a defined date range.
Open download builder →APIs
Selected security histories, snapshots, rankings, and chart-ready data through documented endpoints.
SQL Databases
Client-accessible relational or analytical tables for direct integration into institutional workflows.
Custom Calculations
Private universes, benchmarks, windows, formulas, classifications, and historical backfills.
Institutional data · bulk downloads
Build a research-ready CSV from the Echo archive.
Choose one or more securities, select historically available weekly, monthly, or point-in-time metrics, define the observation window, and export the result in a consistent long format.
Download builder
Select the exact histories your research requires.
The prototype supports up to 25 securities and 20 metrics per request. Blank date fields return the full available history. Mixed-frequency selections are preserved with explicit dataset and frequency columns.
1 Securities→2 Metrics→3 Dates→4 CSV
Step 1
Up to 25Choose securities
Search by ticker or company name, then add each security to the request.
Step 2
Up to 20Choose metrics
Search the downloadable history catalog and add weekly, monthly, or point-in-time series.
Open this page to load the downloadable metric catalog…
Step 3
OptionalChoose observation dates
Step 4
Generate CSV
Select at least one security and one metric.
Output columns: date, ticker, company, sector, dataset, frequency, field, metric, family, output type, unit, and value.
Prototype and licensing boundary
This local download builder demonstrates Echo’s delivery architecture. Availability in the prototype does not by itself grant redistribution rights. Production delivery remains subject to source-vendor licensing, client entitlements, legal review, and the applicable data agreement.
Legal and risk disclosure
Disclaimer & Important Disclosures
Important information about Echo research, calculated data, model outputs, hypothetical results, AI-assisted content, and use of this prototype.
Last updated July 18, 2026 · Prototype disclosure for legal review before commercial distribution1. General information only
All material on this website—including security pages, rankings, screens, charts, model portfolios, research notes, downloadable files, and commentary—is provided solely for educational, informational, and illustrative purposes. Nothing on this website constitutes personalized investment, legal, tax, accounting, or other professional advice.
2. No offer, solicitation, or fiduciary relationship
Nothing on this website is an offer to sell, a solicitation of an offer to buy, or a recommendation concerning any security, investment strategy, advisory service, or financial product. Accessing or using Echo does not create an investment-advisory, brokerage, fiduciary, client, or other professional relationship with Alpha Equity Research or Paul Fekula.
3. Impersonal systematic research
Echo is designed as an impersonal systematic research and analytics platform. Its outputs are generated from common rules, data transformations, formulas, and cross-sectional comparisons and are not tailored to any user's objectives, financial condition, risk tolerance, tax circumstances, or investment horizon. A high or low score is not a buy, sell, or hold recommendation.
4. Data sources, availability, and accuracy
Prototype data may be obtained from public or third-party sources and transformed through automated scripts. Sources may include market-data services, SEC EDGAR filings, FRED, issuer materials, and locally maintained research files. Data may be delayed, revised, incomplete, incorrectly mapped, affected by corporate actions, or unavailable for certain securities or periods. Alpha Equity Research does not warrant that any data, calculation, classification, date, or output is accurate, complete, current, or suitable for a particular purpose.
5. Rankings, scores, and model outputs
Ranks, percentiles, Z-scores, composite scores, screens, factor signals, and model baskets are mathematical research outputs based on selected inputs, formulas, lookback windows, eligibility rules, and assumptions. Results can change materially when data, definitions, universes, transaction-cost assumptions, or model specifications change. Rankings describe relative model conditions, not expected returns or probabilities of profit.
6. Hypothetical and simulated performance
Any backtest, historical simulation, model return, risk statistic, portfolio construction, or hypothetical illustration does not represent the performance of an actual client account or managed portfolio. Simulated results benefit from hindsight and may be affected by selection, survivorship, look-ahead, data-mining, model-sensitivity, liquidity, capacity, and implementation assumptions. Actual trading would incur costs, spreads, market impact, taxes, operational constraints, and other effects that may not be reflected. Past, hypothetical, or simulated performance is not indicative of and does not guarantee future results.
7. AI-assisted content
Echo may use artificial intelligence or automated language tools to organize, summarize, or describe information. AI-assisted text may omit material facts, misstate context, or contain errors. It should be treated as an aid to research—not as verified primary-source analysis—and independently checked against filings, market data, and other authoritative sources before use.
8. Investment risk
All investing involves risk, including loss of principal. Securities may be volatile, illiquid, difficult to value, or unsuitable for a particular investor. Diversification, factor exposure, quantitative methods, and risk controls cannot eliminate losses. Users should consult appropriately qualified professionals and conduct their own due diligence before making any investment decision.
9. Third-party rights and downloads
Names, trademarks, market data, issuer information, and other third-party content remain the property of their respective owners. The availability of a field or downloadable file in this prototype does not grant redistribution, republication, commercial-use, or sublicensing rights. Production access and delivery may be subject to vendor licenses, client entitlements, confidentiality obligations, and separate agreements.
10. No warranty; limitation of responsibility
Echo and all related content are provided on an “as is” and “as available” basis without warranties of any kind. To the fullest extent permitted by applicable law, Alpha Equity Research and its contributors disclaim responsibility for losses, damages, trading decisions, missed opportunities, data errors, interruptions, or other consequences arising from reliance on or use of the website. Users assume full responsibility for how they interpret and use the information.
11. Changes and professional review
Research methods, datasets, formulas, site features, and these disclosures may change without notice. This prototype disclaimer is intended to provide broad risk disclosure but is not a substitute for legal and compliance review. Formal terms of service, privacy disclosures, data agreements, and jurisdiction-specific language should be approved by qualified counsel before commercial launch or broader distribution.
Products & pricing
Research access that scales from individual analysis to institutional data.
Prototype pricing is illustrative and subject to data licensing, legal review, coverage, and client requirements.
Research
Weekly
$500/yr
Weekly research notes and selected score tables.
- Research archive
- Selected rankings
- Email delivery
Analytics
Professional
$2,000/yr
Security pages, dedicated metric histories, rankings, and individual downloads.
- Research portal
- Metric histories
- Latest 12 dated observations
- Stock screener
Data
Enterprise
Custom
Bulk US-equity data, APIs, and integration support.
- Historical packages
- Ongoing updates
- API access
- Data documentation
Bespoke
Institutional
Custom
Private calculation, curation, and research services.
- Custom universes
- Private formulas
- Historical backfills
- Research support