at Financial Technnology Year
Please contact them if you have any questions.
SigTech Quant Platform from SigTech
End-to-end quantitative investment platform for strategy design, backtesting, and implementation. Offers extensive historical data, sophisticated backtesting capabilities, factor modeling, and risk analysis with cloud-based computing infrastructure.
Product analysis by function
Quantitative Analysis Tools for Investment Research & Analysis
Software for statistical analysis, econometric modeling, and quantitative research to identify patterns, correlations, and potential investment opportunities based on historical data and mathematical models.
More Quantitative Analysis Tools
More Investment Research & Analysis ...
Data Integration & Management
(9 Yes /9 Known /10 Possible features)
|
Multi-source Data Import Ability to import data from various internal and external sources including APIs, CSVs, Excel, and databases. |
SigTech supports ingesting data from a wide variety of data sources, including APIs and files, as publicized in their 'data onboarding' documentation. | |
|
Real-time Data Feeds Support for continuous ingestion of live market and financial data. |
SigTech highlights real-time market data support and continuous data ingestion for strategy implementation. | |
|
Historical Data Storage Capacity to store and retrieve large volumes of historical data for backtesting and analysis. |
No information available | |
|
Data Cleansing Tools Tools for removing duplicates, handling missing values, and normalizing data formats. |
Data cleaning and normalization is a feature in SigTech's standard onboarding and pipeline workflow. | |
|
Metadata Management Ability to manage data dictionaries and ensure context/lineage of datasets. |
SigTech includes metadata management, such as data dictionaries and lineage, as described in technical product docs. | |
|
Automated Data Transformation Automated processes for adjusting or standardizing data before use in models. |
Automated data transformation is built into SigTech’s onboarding pipeline, enabling standardization and preprocessing required for backtesting. | |
|
Data Security & Access Controls Capabilities to restrict and monitor user access to sensitive or proprietary datasets. |
Role-level access controls and data security settings are integral to SigTech’s enterprise solution. | |
|
Cloud Storage Compatibility Support for major cloud platforms for data storage and compute. |
SigTech is a cloud-native platform, supporting major cloud storage providers (AWS, Azure, GCP). | |
|
Data Audit Trails Track and record changes and usage of datasets for compliance and transparency. |
The platform tracks and manages changes to datasets for compliance and auditability. | |
|
Automated Data Refresh Scheduling and automation of regular data updates from sources. |
SigTech facilitates automated periodic data refreshes from designated sources. |
Statistical & Quantitative Analysis
(10 Yes /10 Known /10 Possible features)
|
Descriptive Statistics Generate measures such as mean, median, standard deviation, skewness, and kurtosis across datasets. |
Descriptive statistics and data profiling are available in the quant research toolkit. | |
|
Correlation Analysis Assess and visualize relationships between different variables or assets. |
SigTech provides users with tools for analyzing correlations between multiple assets. | |
|
Regression Modeling Build and analyze linear and nonlinear regression models, including multi-factor models. |
Users can build and analyze regression models using integrated Python libraries and platform modules. | |
|
Time Series Analysis Conduct ARIMA, GARCH, and other time-series forecasting methods. |
The platform supports time series analysis and forecasting, including ARIMA/GARCH, as part of its quant analytics stack. | |
|
Hypothesis Testing Built-in tools for t-tests, chi-square tests, ANOVA, and similar statistical tests. |
Hypothesis testing tools are available through the included Python (SciPy, statsmodels) integration. | |
|
Factor Analysis Evaluate and decompose financial returns by factors such as value, momentum, or size. |
Factor analysis for returns decomposition (e.g., Fama-French, risk premia) is a core offering. | |
|
Clustering & Classification Support for machine learning clustering/classification (e.g., k-means, decision trees). |
ML-based tools such as clustering/classification are available via Jupyter/Python unification. | |
|
Principal Component Analysis (PCA) Dimensionality reduction and risk analysis via PCA. |
Dimensionality reduction and PCA are explicitly supported within the toolkit. | |
|
Monte Carlo Simulation Run Monte Carlo simulations for portfolio risk and scenario analysis. |
Monte Carlo simulation methods are provided for portfolio and risk analysis per platform documentation. | |
|
Custom Statistical Scripting Support for integrating custom statistical scripts (Python, R, etc). |
Researchers can run custom scripts in Python via JupyterLab environment and the SigTech API. |
Econometric Modeling
(10 Yes /10 Known /10 Possible features)
|
Panel Data Analysis Support for econometric techniques using cross-sectional/time-series panel data. |
Panel data and econometric analysis are enabled via support for common Python stats packages. | |
|
Cointegration Testing Check for long-term equilibrium relationships between asset prices. |
Cointegration testing is available via integrated statistical packages. | |
|
Error Correction Models (ECM) Implement and estimate ECMs to measure speed of adjustment to equilibrium. |
Error Correction Model support indirect through Python econometric libraries. | |
|
Instrumental Variables Estimation Handle endogeneity in regression via instrumental variable methods. |
IV estimation (instrumental variables) is achieved through access to Python (statsmodels) from within the platform. | |
|
Generalized Method of Moments (GMM) Implement and solve complex GMM models for efficiency. |
GMM is accessible to researchers through the integrated Python scientific stack. | |
|
ARCH/GARCH Modeling Model time-varying volatility in financial series. |
ARCH/GARCH modeling is a highlighted feature for time-series risk modeling. | |
|
Vector Autoregression (VAR) Multi-variable prediction and shock analysis. |
VAR modeling is available through Python libraries supported in SigTech’s notebooks. | |
|
Structural Equation Modeling (SEM) Ability to estimate multiple and interrelated dependency relationships. |
SEM is supported via statistical libraries accessible in the Jupyter environment. | |
|
Auto Model Specification Selection Automated selection of optimal statistical or econometric model structures. |
Auto model selection is possible in pipeline design via Python-based workflow automation. | |
|
Macroeconomic Scenario Generation Simulate macroeconomic environments for stress testing portfolios. |
Macroeconomic scenarios for stress-testing are available and can be integrated into portfolio analysis. |
Portfolio Construction & Optimization
(10 Yes /10 Known /10 Possible features)
|
Mean-Variance Optimization Classical Markowitz portfolio optimization. |
Mean-variance optimization and Markowitz methodology are documented as supported optimization functions. | |
|
Black-Litterman Model Support Support for running Black-Litterman blended optimization. |
The Black-Litterman model is specifically called out as included in SigTech’s portfolio tools. | |
|
Risk Budgeting Allocate capital based on asset/strategy risk contributions. |
Risk budgeting via marginal contribution analysis and similar tools provided in optimization workflows. | |
|
Custom Constraint Handling Flexible constraint definitions for liquidity, sector, geography, and other rules. |
Custom constraints can be applied in portfolio construction, including liquidity, sector, and geographic rules. | |
|
Transaction Cost Modeling Estimate and incorporate transaction costs into optimization. |
Transaction cost modeling is integrated into optimization, per product docs. | |
|
Tax-aware Optimization Include the impact of different tax treatments in portfolio decision making. |
Tax impact calculations are enabled by customizable portfolio analytics scripting. | |
|
Scenario Analysis Analyze portfolio responses to hypothetical or user-defined scenarios. |
Scenario analysis is a core component within strategy and risk modules. | |
|
Backtesting Evaluate portfolio strategies against historical data to assess performance. |
Backtesting is a central feature — SigTech was built for this purpose. | |
|
Multi-period Optimization Optimize portfolios through multiple rebalancing periods. |
Multi-period optimization including scenario and rebalance simulation supported. | |
|
ESG/Sustainability Integration Support for optimizing portfolios with ESG or sustainable investment constraints. |
ESG integration is available through customizable constraints and ESG data sources. |
Risk Measurement & Management
(10 Yes /10 Known /10 Possible features)
|
Value at Risk (VaR) Calculation Calculate portfolio or asset VaR using various methods (historical, parametric, Monte Carlo). |
Value at Risk calculation features are standard in the platform's risk module. | |
|
Conditional VaR (CVaR) Support for tail risk calculations using CVaR. |
Conditional VaR (CVaR) is included in tail risk analysis modules. | |
|
Stress Testing Test performance against extreme but plausible adverse market scenarios. |
Stress testing is natively integrated, with the ability to simulate adverse market scenarios. | |
|
Sensitivity Analysis Analyze the impact of changes in risk factors (e.g., interest rates, spreads). |
Sensitivity analysis is possible on interest rates, spreads, and custom risk factors. | |
|
Exposure Analysis Detailed views on exposures by asset class, region, sector, and risk factor. |
Portfolio and position-level exposure analysis is supported with interactive dashboards. | |
|
Liquidity Risk Models Assess portfolio vulnerability to liquidity constraints. |
SigTech supports liquidity risk assessment and liquidity-driven scenario modeling. | |
|
Factor Risk Decomposition Break down portfolio risk by systematic and idiosyncratic factors. |
Factor risk decomposition is available, cross-referencing factor exposures and idiosyncratic risks. | |
|
Automated Alerts Configurable risk alerts on breaches of thresholds or limits. |
Automated and configurable alerts for risk thresholds and portfolio breaches available via dashboard. | |
|
Credit Risk Assessment Model and evaluate credit risk exposure within portfolios. |
Credit risk assessment via integrated analytics and Python capabilities. | |
|
Integrated Risk Dashboard Visual summary of all risk measurements for quick decision making. |
Unified risk dashboard is highlighted as a key management and reporting interface. |
Performance Measurement & Attribution
(10 Yes /10 Known /10 Possible features)
|
Time-weighted & Money-weighted Returns Generate both TWR and IRR-style returns for portfolios and benchmarks. |
Built-in performance measurement supports both time-weighted and money-weighted returns. | |
|
Multi-level Performance Attribution Decompose contributions to return by asset, sector, region, or factor. |
Multi-level performance attribution available for assets, sectors, and custom groupings. | |
|
Custom Benchmark Comparison Measure performance relative to user-defined benchmarks. |
SigTech enables comparison against user-defined (and index) benchmarks. | |
|
Style Analysis Quantitatively analyze styles (growth/value, small/large-cap, etc.). |
Style analysis (e.g., growth/value) is possible with supplied analytics modules. | |
|
Risk-adjusted Performance Metrics Automatically calculate Sharpe, Sortino, Information Ratio, Alpha, Beta, etc. |
Risk-adjusted performance metrics such as Sharpe and Sortino ratios are provided. | |
|
Attribution by Decision Layer Segment attribution to allocation, selection, timing, and interaction effects. |
Attribution by decision layer available through contribution analytics functions. | |
|
Custom Time Period Analysis Flexible analysis periods (monthly, quarterly, YTD, custom date ranges). |
Flexible analysis periods, including custom date range queries built into reporting tools. | |
|
Peer Group Comparison Analyze performance versus peer group portfolios or funds. |
Peer group comparison is supported via importing external performance data sets. | |
|
Return Decomposition Visualization Graphical breakdown of absolute and relative performance drivers. |
Visualization components provide return decomposition and performance drivers. | |
|
Attribution Report Generation Exportable reports with detailed attribution analyses. |
Automatically generated attribution reports are available for download. |
Visualization & Reporting
(10 Yes /10 Known /10 Possible features)
|
Customizable Dashboards User-defined dashboards for quantitative analytics and monitoring. |
User-customizable dashboards for different analytic modules are featured. | |
|
Interactive Charting Dynamic charts (line, bar, scatter, heatmap, etc.) allowing deep data exploration. |
Interactive charting options (line, bar, scatter, heatmap) build upon Python and dashboard libraries. | |
|
Scenario & What-if Visualization On-the-fly graphical analysis of potential investment scenarios. |
Scenario and what-if visualization modules for portfolio simulations are documented capabilities. | |
|
Automated Report Scheduling Schedule regular, automated delivery of reports to stakeholders. |
Report scheduling and automatic email delivery are supported in platform's reporting suite. | |
|
Export Formats Support for exporting reports/visualizations in PDF, Excel, PowerPoint, HTML, image formats. |
Export to PDF, Excel, PowerPoint, HTML, and images indicated in user documentation. | |
|
Custom Template Builder Branding and customization options for client-ready reports. |
Customizable report templates enable white-label output for clients. | |
|
Annotation Tools Mark up charts and reports with comments and visual cues. |
Annotation tools available for annotated charts and reports. | |
|
Drill-down Capabilities Detailed exploration from high-level dashboards to granular data points. |
Dashboards support drill-down to individual trades, assets, and sub-portfolios. | |
|
Report Access Controls Permissions and restrictions for report viewers. |
User-based permissions for viewing and exporting reports are available. | |
|
Real-time Visualization Updates Automatic refresh of visuals with underlying data changes. |
Visualizations update in real time when underlying data changes, e.g., post-backtest. |
Automation & Workflow
(9 Yes /9 Known /10 Possible features)
|
Workflow Orchestration Design, schedule and automate common quantitative analysis processes. |
Common analysis pipelines can be scheduled and automated for quant/ops workflows. | |
|
Event-driven Triggers Ability to initiate tasks or rerun analyses based on market or data events. |
Event-driven automation for rerunning analysis after data updates is available. | |
|
Batch Processing Support for high-volume batch analysis jobs. |
Batch processing of large analysis jobs supported by cloud compute environment. | |
|
Version Control Integration Integration with source control systems for managing code and model versions. |
SigTech integrates with GitHub and internal versioning for code/notebooks/models. | |
|
Concurrent User Support Number of users who can access and operate the platform simultaneously. |
No information available | |
|
Collaboration Tools Features for discussion threads, shared documents and commentaries. |
Collaboration features include sharing notebooks, comments, and result objects between users. | |
|
Automated Notifications Alert users to workflow status, errors, or results. |
Automated and user-triggered notification system about workflow/run status provided. | |
|
Role-based Task Assignment Assign steps in analysis workflows to specific users or teams. |
Role-based workflow assignments supported for team and project collaboration. | |
|
API-based Task Automation Automate analysis tasks via API calls. |
Platform supports API-driven workflow automation as documented for enterprise users. | |
|
Audit Logging Comprehensive tracking of workflow executions. |
Comprehensive audit logging of analysis tasks and user actions is built-in. |
Integration & Extensibility
(10 Yes /10 Known /10 Possible features)
|
API Support Comprehensive APIs for importing/exporting data and calling analytical routines. |
Extensive API documentation suggests broad API support for data and analytics integration. | |
|
Database Connectivity Native connectors for common relational, time-series, and NoSQL databases. |
Native connectors for SQL, time-series, and NoSQL databases are supported. | |
|
Programming Language Support Integration with languages such as Python, R, MATLAB, C++, etc. |
Supports Python, with R, MATLAB, and other language integrations available as plug-ins or via API. | |
|
Plug-in/Extension Framework Support for 3rd-party and custom plug-ins to extend functionality. |
Plug-in framework for custom analysis and extensions is supported (private customer API). | |
|
Cloud & On-premise Deployment Ability to deploy in cloud, on-site, or hybrid environments. |
Supports both cloud-native and client-hosted (on-premise/hybrid) deployments. | |
|
Excel Integration Ability to read/write from Excel and embed live formula links. |
Deep Excel connectivity for importing/exporting and maintaining live links to models. | |
|
Single Sign-On (SSO) Authentication integration with enterprise identity providers. |
Single Sign-On (SSO) integration with enterprise identity providers is available. | |
|
Mobile Access Mobile app or responsive web UI support. |
Responsive web UI supports mobile access to platform dashboards and analyses. | |
|
Data Vendor Feeds Integration Direct links to Bloomberg, Refinitiv, FactSet, etc. |
Direct datafeeds (Bloomberg, FactSet, etc) are available or can be added via vendor APIs. | |
|
Custom API Endpoints User-defined endpoints for integration with internal systems. |
Enables users to define custom API endpoints for internal system integration. |
Speed, Scalability & Performance
(8 Yes /8 Known /10 Possible features)
|
Computation Speed Performance of core analytics and model calculations. |
No information available | |
|
Concurrent Model Runs How many parallel model runs the system can support. |
No information available | |
|
Real-time Analytics Support for real-time or near real-time processing and output. |
Platform offers real-time and near-real-time analytics outputs for supported datasets. | |
|
Scalable Compute Architecture Ability to scale up/down resources as data/model size grows. |
Cloud-native scalable infrastructure supports resizing compute resources as needed. | |
|
Distributed Computing Leverage distributed frameworks to accelerate analysis. |
Distributed/cloud computing infrastructure built-in for parallel analyses. | |
|
Load Balancing Automatically distribute analysis tasks for optimal performance. |
Analysis jobs distributed automatically for load balancing in cloud deployments. | |
|
Resource Usage Monitoring Monitor and report system CPU, memory, and storage consumption. |
Resource monitoring dashboards available to track compute/storage. | |
|
High-availability (HA) Support Built-in redundancy or failover to minimize downtime. |
High-availability and failover options are standard in enterprise-level cloud-native applications. | |
|
Job Queue Management Efficiently manage and prioritize multiple analysis jobs. |
Job queue management available for prioritizing analyses and runs. | |
|
Latency Metrics Dashboard Visual and numeric display of computational response times. |
Response time metrics and latency dashboards are present for monitoring performance. |
Security & Compliance
(10 Yes /10 Known /10 Possible features)
|
Data Encryption (At Rest/In Transit) Protects sensitive data via encryption during storage and network transfer. |
Platform uses industry-standard encryption both at rest and in transit. | |
|
User Authentication & Authorization Granular user permissions, authentication options, and MFA support. |
Authentication, granular permissions, and MFA are offered in enterprise settings. | |
|
Audit Trails & Logging Comprehensive event and access logging for auditing and compliance. |
Comprehensive audit log covering both analysis events and user/system access. | |
|
Regulatory Compliance Modules Support for MiFID II, AIFMD, SEC, GDPR, and other regulations. |
SigTech states regulatory compliance support, including MiFID II, SEC, and GDPR modules for relevant operations. | |
|
Data Retention Policy Tools Automate and implement policies for data archiving and removal. |
Data retention policies and automation features are implemented as part of enterprise data governance. | |
|
Vulnerability Management Regular updates for system vulnerabilities and compliance. |
Regular vulnerability scanning and patch management described in security overview. | |
|
Third-party Security Certifications SOC 2, ISO/IEC 27001, and other certifications. |
SigTech advertises SOC 2 and ISO/IEC 27001 certifications for the platform. | |
|
Secure API Gateways Ensure API connections are secure and monitored. |
Secure API gateways and monitored connections are described in security and IT documentation. | |
|
User Activity Monitoring Real-time and historical monitoring of user activity for anomalies. |
User activity is monitored in real time and logs are auditable for anomalies. | |
|
Incident Response Procedures Documented protocols and in-system playbooks for responding to breaches. |
Incident response procedures and playbooks are outlined as part of SigTech's operational security. |
This data was generated by an AI system. Please check
with the supplier. More here
While you are talking to them, please let them know that they need to update their entry.