Automations
This pillar focuses on autonomous trading workflows that ingest price feeds, earnings transcripts, alternative data, and market sentiment to trigger portfolio actions with minimal manual latency. Content clusters should explore signal generation, execution routing, reconciliation, guardrail design, and post-trade risk monitoring for hedge funds, prop desks, and institutional quant teams pursuing faster decision velocity and tighter operational control.
This foundational page details the end-to-end architecture for building a custom, autonomous quant trading system. It covers the orchestration of signal generation, execution routing, risk guardrails, and post-trade reconciliation into a single, low-latency workflow, delivering faster decision velocity and tighter operational control for hedge funds and prop desks.
This page explains how to build a workflow where specialized agents continuously ingest and normalize disparate data feeds—price, news, options flow, alternative data—to generate a unified, actionable signal. The architecture focuses on data pipeline resilience, latency management, and the agentic logic required to resolve conflicting signals before they reach execution.
This page details a custom workflow that parses earnings calls and SEC filings in real-time, extracting sentiment, forward guidance, and key event timelines using LLMs. It shows how to integrate this unstructured data stream into quantitative models to capture alpha before manual analysts can react, improving research throughput.
This page covers the build for automating the collection, cleaning, and feature engineering of non-traditional data like credit card transactions, satellite imagery, or web traffic. It focuses on the orchestration layer needed to transform raw, noisy data into validated trading signals while managing data vendor APIs and quality checks.
This page outlines the architecture for a system that continuously monitors relationships across equities, FX, rates, and commodities to detect shifting market regimes. It explains how to automate the recalibration of portfolio hedges and strategy weights based on these signals, reducing risk during volatile transitions.
This page describes a custom workflow that automates the end-to-end backtesting process: from pulling historical data and generating simulated fills to running performance attribution and generating compliance reports. It highlights how this accelerates research cycles and improves model validation before live deployment.
This page details the build for an automated pipeline that creates, validates, and version-controls feature datasets for quant models. It covers the orchestration of data transformations, outlier detection, and train-test splits, eliminating manual, error-prone spreadsheet work for research teams.
This page explains how to implement a sandbox environment where new strategies are automatically paper-traded. It covers the agents that simulate market impact, manage virtual capital, track performance against benchmarks, and trigger alerts for calibration or escalation before real capital is deployed.
This page outlines the architecture for an execution system that dynamically slices large orders based on real-time liquidity, volatility, and market impact models. It details the agentic logic for choosing between VWAP, TWAP, or liquidity-seeking schedules to minimize transaction costs autonomously.
This page covers the custom build for a smart order router that evaluates lit exchanges, dark pools, and crossing networks in real-time. It explains the decision logic for venue selection, routing around latency arbitrage, and managing order types to achieve best execution while adhering to complex best-exposure rules.
This page details a workflow where an AI agent monitors real-time market microstructure—volatility, spread, volume—and automatically selects the optimal execution algorithm (aggressive, passive, dark) for each order. It ties the build to measurable savings in implementation shortfall.
This page explains how to build a TCA system that provides immediate feedback on execution quality. It details the workflow for comparing achieved price to benchmarks, analyzing market impact, and using those insights to dynamically adjust ongoing orders or tune future execution logic.
This page outlines the architecture for embedding automated, real-time risk checks directly into the order generation pipeline. It covers checks for position limits, sector exposures, counterparty credit, and regulatory filters (OFAC), ensuring every proposed trade is pre-vetted before reaching the market.
This page details a continuous monitoring workflow that aggregates exposures across strategies, asset classes, and geographies. It explains how to build alerting and automatic circuit-breakers that prevent breaches of internal or regulatory concentration limits, protecting the firm from unintended risk buildup.
This page covers the build for a surveillance system that uses agentic workflows to detect potential spoofing, layering, or insider trading patterns across order and trade data. It focuses on the logic for scoring alerts, gathering context, and routing high-risk cases to compliance officers with an audit trail.
This page explains how to automate the continuous calculation of Value-at-Risk and scenario-based stress tests on the live portfolio. It details the architecture for running these compute-intensive jobs, setting dynamic thresholds based on volatility, and triggering immediate risk manager alerts and potential position reduction.
This page details the build for straight-through processing of trade reconciliations between internal books, prime brokers, and custodians. It covers the agents that match trades, identify breaks, classify exceptions (DKs), and route them for resolution, drastically reducing operational risk and manual middle-office labor.
This page outlines a predictive workflow that analyzes trade details, counterparty history, and market settlement data to flag trades at high risk of failing. It explains the automation for pre-emptively notifying operations, sourcing alternative stock, or initiating buy-ins, improving settlement rates and reducing costs.
This page covers the architecture for a system that continuously attributes P&L to its drivers—market move, currency, carry, trading activity—in real-time. It details how to build an explainable layer that helps portfolio managers understand daily performance without waiting for end-of-day batch processes.
This page explains the build for a system that monitors portfolio weights against target allocations, calculates the optimal trade list to minimize market impact and tax consequences, and automatically routes orders for execution. It focuses on the guardrails and approval gates required for trusted autonomy.
This page details a custom workflow that continuously scans the portfolio for tax-loss harvesting opportunities across jurisdictions. It covers the logic for identifying wash-sale rule violations, simulating net tax impact, and generating compliant trade recommendations to improve after-tax returns automatically.
This page outlines the architecture for a system where agents monitor the portfolio's exposure to style factors (value, momentum, quality) versus a target. It automates the generation of hedging or tilting trades to maintain desired factor bets, helping quant funds execute their investment thesis more precisely.
This page details a workflow that ingests macroeconomic indicators, central bank communications, and geopolitical events to adjust strategic asset allocation weights. It explains the orchestration of signal interpretation, portfolio construction, and the generation of rebalancing instructions for multi-asset funds.
This page covers the build for a system that automatically executes hedging overlays—using futures, options, or swaps—based on portfolio risk metrics. It details the triggers for delta-hedging an options book or implementing tail-risk protection, reducing manual oversight and improving hedge timing.
This page explains the architecture for a dashboard-in-code that streams live risk metrics—Greeks, VaR, stress P&L—to risk managers. It focuses on the data pipelines, calculation engines, and alerting logic required to move from daily batch risk reporting to a continuous monitoring posture.
This page details a workflow that aggregates exposure across swaps, repos, and prime brokerage relationships with each counterparty. It explains how to build a centralized view, calculate netting benefits, and automate regulatory exposure reports (e.g., Form PF), reducing manual aggregation errors.
This page outlines the build for a system that projects future cash flows from trades, margin calls, and financing activities. It uses agents to simulate stress scenarios, identify potential funding shortfalls, and trigger pre-emptive actions like collateral optimization or term repo issuance.
This page covers the implementation of an anomaly detection system that monitors live P&L, trade rates, and system logs for unusual patterns indicative of errors or fraud. It details the machine learning models, baselining logic, and escalation workflows that protect the firm's capital and integrity.
This page details the architecture for achieving true STP, where a confirmed trade flows from execution through to settlement without manual touchpoints. It covers the integration points with OMS, EMS, and settlement systems, and the exception-handling logic for non-standard trades that break the flow.
This page explains how to automate the entire margin call lifecycle: parsing broker notices, reconciling them against internal calculations, initiating disputes if variances exceed thresholds, and executing collateral movements. This workflow reduces operational risk and frees up treasury staff for strategic work.
This page covers the build for a system that automatically ingests corporate action announcements (mergers, splits, dividends), determines portfolio entitlements, and makes election decisions based on pre-set rules. It eliminates manual tracking errors and ensures accurate position and cash accounting.
This page outlines a workflow where agents monitor financing rates, stock loan availability, and service quality across multiple prime brokers. It automates the analysis for allocating business, negotiating rates, and generating performance reports, helping multi-prime funds optimize their financing costs.
This page details the automation of the complex, error-prone process of calculating management and performance fees for a fund. It covers the integration with portfolio accounting systems, the application of high-water marks and hurdles, and the generation of accurate, audit-ready investor statements and invoices.
This page explains how to build a system that transcribes, analyzes, and flags potentially non-compliant communications from chat, email, and voice channels. It focuses on the LLM-based context understanding, pattern matching for insider information, and the secure audit trail required for regulatory examinations.
This page details a workflow that automates the assembly of a complete, time-stamped audit trail for any given trade—from initial research signal to final settlement—in response to regulatory inquiries. It drastically reduces the manual, multi-day effort typically required for such requests.
This page covers the architecture for a system that continuously monitors the health, latency, and completeness of market data feeds. It automates the detection of stale ticks or feed drops, triggers alerts, and executes failover to backup feeds or data centers to protect trading strategies.
This page outlines a DevOps-style workflow for quant trading, where agents manage the deployment of new strategy code, monitor for performance regressions or errors in production, and automatically roll back to a stable version if breaches are detected, ensuring system resilience.
This page explains how to build a workflow that monitors compute and data egress costs for cloud-based trading and research infrastructure. It uses agents to right-size instances, spin down idle research clusters, and forecast bills, directly tying infrastructure spend to trading activity and P&L.
This page details the build for a system that validates the integrity of every step in the quant data pipeline—from raw ingestion to feature storage. It automates checks for missing values, schema drift, and statistical outliers, ensuring model inputs are reliable and triggering alerts for data engineers.
This page covers a custom workflow for hedge funds that continuously compares financing rates, stock loan availability, and cross-margining benefits across prime brokers. It automates the analysis to recommend and sometimes execute shifts in balances or trades to minimize funding costs and maximize efficiency.
This page details the build for a system that automates the creation of tailored investor materials—tear sheets, letters, performance attribution—from portfolio and CRM data. It streamlines the capital-raising process for fund managers by ensuring consistent, timely, and accurate communication with prospective and current investors.
This page outlines the architecture for a fund-of-funds or multi-strategy pod shop to automatically allocate risk capital and attribute P&L back to individual strategies. It covers the logic for calculating risk-adjusted returns, managing internal funding rates, and generating fair compensation reports.
This page explains the build for a system that provides nanosecond-level monitoring of strategy decision latency, exchange response times, and network hops. It automates the detection of latency spikes, correlates them with P&L impact, and helps engineers pinpoint and resolve infrastructure bottlenecks.
This page details the custom workflow for an automated market-making system that continuously quotes bids and offers on options or futures. It covers the real-time calculation of Greeks, inventory risk management, and the dynamic adjustment of spreads based on volatility and competition, aiming to capture the spread profitably.
This page outlines the architecture for an institutional workflow that automates the creation and daily rebalancing of portfolios to track an index or a custom client-specified basket. It focuses on minimizing tracking error through optimized trade lists and handling corporate actions seamlessly.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
We understand the task, the users, and where AI can actually help.
Read more02
We define what needs search, automation, or product integration.
Read more03
We implement the part that proves the value first.
Read more04
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us
