Institutional-Grade Algorithmic Trading Planning With Purpose
We design investment strategies with a clear objective: aligning your capital with your long-term goals, risk profile, and real-world constraints. Our approach focuses on disciplined decision-making, transparency, and adaptability—ensuring your portfolio evolves as your life and financial priorities change.
Fee-Only, Objective Management
We operate on a fee-only basis, eliminating conflicts of interest and ensuring every decision is made solely in your best interest.
Fiduciary-Driven Investment Decisions
As fiduciaries, we are legally and ethically committed to acting with care, loyalty, and full transparency in managing your assets.
Experience Over Sales
You work directly with investment professionals—not salespeople—focused on analysis, execution, and long-term outcomes.
How Can Our Team Help You to Reach Your Goals
Learning About You
Schedule a 30-minute call with our professional to discuss your goals and how we can help. This phone or Zoom call also outlines who we are and our process.
Organized Meeting
Meet with our team to gather the necessary data for your Financial Plan. We’ll discuss your finances, lifestyle, and goals, including investments, assets, expenses, and income.
Plan Meeting
Our advisor will present your personalized financial plan, ensuring it aligns with your evolving needs and goals, and provide clear steps to help you reach your financial objectives.
Implementation
Your advisor will send a copy of your plan with an actionable list of recommendations. We’ll implement and manage these, keeping you updated.
What is Algorithmic Trading — and why it matters for a fiduciary RIA
Algorithmic trading is the systematic use of rules-based models to make investment and execution decisions with consistency, auditability, and speed. That definition is intentionally plain—because from a fiduciary lens, the real value is not the “flash” of AI, but the control, repeatability, and documentation you gain when human judgment is wrapped in robust process.
In my practice as an SEC-registered RIA, algorithms don’t replace judgment; they encode policy. When a client’s IPS sets a 60/40 target with tracking-error limits, guardrails for drawdown, and tax constraints, an algorithm becomes the always-on policy engine that watches exposures, flags drifts, and proposes compliant actions.
This is the difference between “automation for speed” and “automation for fiduciary consistency.”
Three reasons this matters to RIAs:
- Time-scale: Markets move faster than quarterly review cycles. Systematic monitors reduce the window where portfolios can deviate from mandate.
- Traceability: Every rebalance, harvest, or execution choice can be logged and retraced—vital for client trust and regulatory review.
- Personalization at scale: Constraints (ESG preferences, wash-sale windows, legacy positions) can be honored systematically without manual spreadsheet fatigue.
I learned this the useful way: when we moved from “calendar rebalancing” to signal-based daily checks, our discussions with HNW clients shifted from “Why did we miss X?” to “Show me the rule that triggered Y.” That’s a better conversation—grounded in policy, not hindsight.
Algorithmic Trading Systems & Institutional Execution — From RIA Portfolios to Algorithmic Trading Hedge Funds
Most RIA-friendly algorithms fall into two broad buckets:
- Investment algorithms (what to own): factor tilts (value, quality, momentum, low-vol), mean reversion, defensive sleeves, or multi-asset signals.
- Execution algorithms (how to trade): VWAP, TWAP, POV, Implementation Shortfall, and smart order routing (SOR).
For fiduciaries, execution is not an afterthought—it’s part of your duty of best execution. Here’s a compact matrix we use when educating clients and internal reviewers:
| Algo | Core idea | Best for | Pros | Watch-outs |
|---|---|---|---|---|
| VWAP | Match day’s average price | Liquid names over full session | Minimizes market footprint vs intraday mean | Can telegraph schedule; not ideal in news-heavy days |
| TWAP | Even slices over time | Thin liquidity; stable flows | Simple, predictable, easy to audit | Ignores liquidity spikes; may miss favorable prints |
| POV | Trade as % of live volume | High-volume names with variable flow | Adapts to liquidity; reduces slippage | Needs caps/guards to avoid chasing spikes |
| Impl. Shortfall | Optimize vs decision price | Urgent or alpha-sensitive orders | Balances speed vs cost dynamically | Requires robust pre-trade models & oversight |
In my shop, we pair signal-driven order creation with SOR across venues, adding circuit breakers that slow/stop participation if spreads widen abnormally or if a security hits volatility bands. I’ve seen that simple control save clients from avoidable slippage on event days.
Algorithmic Trading Systems & Institutional Execution: From Trend-Following to VWAP, TWAP and POV
Most RIA-friendly approach
For RIAs, the most effective algorithmic trading systems are not those optimized for speed or speculative alpha, but those designed for control, auditability, and repeatability.
In practice, this means prioritizing execution frameworks and investment rules that can be clearly explained to clients, reviewed by compliance, and adjusted without introducing hidden risks.
RIA-friendly systems typically emphasize transparent execution algorithms (such as VWAP, TWAP, and POV), clearly defined investment signals, and conservative safeguards over aggressive optimization.
The goal is not to compete with high-frequency desks or proprietary hedge funds, but to deliver institutional-grade discipline aligned with fiduciary duty, best-execution standards, and long-term portfolio objectives.
In this context, algorithmic trading becomes a governance tool: a way to enforce policy, reduce behavioral noise, and ensure consistency across portfolios—rather than a black-box engine chasing short-term market advantages.
The Winning Hybrid Model in Investment Management: Human Fiduciary Advice + Algorithmic Execution
Our model is intentionally hybrid:
- Human core (financial planning, IPS design, risk capacity mapping, tax strategy, suitability review).
- Algorithmic layer (continuous monitoring, rebalancing proposals, tax-loss harvesting checks, execution scheduling, risk alerts).
Practically, that looks like this: I sign off on every policy (what we will do and under what conditions). The system handles surveillance and suggestions (what is drifting, what can be harvested, what orders to schedule), and a human approves material changes. In my experience, framing it this way resonates with committees and HNW decision-makers: control lives with people; consistency lives with code.
Two cultural rules helped us avoid the “AI can do it all” trap:
- We speak in terms of controls and evidence, not performance promises.
- We document assumptions, data sources, and model limits alongside every algorithm—right inside the runbook.
Building multi-factor portfolios: value, quality, momentum and low-volatility
A multi-factor core—implemented with transparent rules—fits the RIA ethos: academically grounded, diversifying, explainable. We maintain three model portfolios (Conservative, Moderate, Aggressive), each with calibrated exposures to value, quality, momentum, and low volatility. The implementation is flexible: ETFs for efficiency, or direct indexing for tax and personalization.
What clients care about is not “factor lore,” but how factors translate to lived outcomes: smoother rides (low-vol), avoiding junk (quality), participating in leadership (momentum), and paying a fair price (value). We show this with attribution reports and factor heatmaps—nothing flashy; just clear decomposition.
A practical tip from the field: multi-factor works better when married to explicit rebalance logic (e.g., drift bands, turnover budgets, and a tax budget) rather than calendar-only schedules. The algorithm proposes; the advisor approves or defers—with the “why” captured for audit.
Systematic rebalancing & tax-loss harvesting: how to capture tax alpha (responsibly)
Done right, rebalancing and TLH aren’t gimmicks; they’re repeatable sources of value. In my practice, daily surveillance surfaces candidates, but execution is paced by wash-sale rules, client-specific constraints, and cost thresholds. That’s how we target the 0.90%–2.35% total annual value-add we’ve observed across clients (combining avoided drift drag, systematic TLH, and lower fees from process efficiency). These are estimates, not guarantees, and we say that explicitly in client materials.
What’s under the hood:
Drift monitoring with tiered bands (smaller for factors, wider for asset classes).
Tax budgets and minimum-benefit thresholds (no nickel-and-diming).
Surrogate lists for TLH to avoid wash-sales while maintaining factor intent.
A simple deferral log: if we override an automated suggestion (common near quarter-end), we document rationale and revisit.
A small anecdote: during a choppy quarter, our system flagged losses in a satellite sleeve while a core position was near long-term gain eligibility. We harvested the sleeve, preserved long-term status elsewhere, and the client could see—in a single page—what changed, why, and the expected tax impact. That clarity matters as much as the dollars.
Daily surveillance surfaces candidates, but execution is strictly paced by Wash-Sale rules, client-specific constraints, and cost thresholds. This systematic approach combines avoided drift drag with operational process efficiency.
Planning With Purpose
True financial planning goes beyond numbers — it starts with clarity, intention, and direction. At Finovate, we help you define what truly matters to you, aligning your financial decisions with your long-term goals and life priorities.
Our purpose-driven approach ensures that every investment strategy is built around your unique vision, whether you’re planning for retirement, growing generational wealth, or navigating life’s major transitions. With disciplined planning and ongoing guidance, we help you move forward with confidence and control.
Algorithmic Trading Infrastructure for Real-Time Risk Monitoring: VaR, CVaR, Drawdowns and Circuit Breakers
Institutional clients expect continuous risk visibility. Our dashboards show VaR/CVaR, ex-ante volatility, max drawdown, beta, and cross-asset correlations, refreshed intraday. The point isn’t to stare at the screen; it’s to alert only when the portfolio meaningfully deviates from its risk mandate.
Controls we treat as non-negotiable:
- Scenario library (rates shock, growth scare, liquidity crunch) with pre-built stress tests.
- Correlation drift alerts to catch regime changes.
- Circuit breakers that suspend trading in a sleeve if model inputs break (e.g., stale prices, corporate actions not yet reflected).
One memorable case: an options-related data disruption widened our estimated spread inputs. The system tripped a breaker; we paused non-urgent orders and avoided pushing trades through a distorted tape. That’s fiduciary risk management expressed in code.
Backtesting & SEC Compliance: Policies, Testing, and Form ADV Disclosures
Compliance is not a post-hoc paragraph; it’s the operating system of an algorithmic RIA.
Rule 206(4)-7 (Compliance Program Requirement) in practice means:
- Written policies & procedures per algorithm: purpose, data, triggers, approvals, rollback plan.
- Testing: backtests with out-of-sample data, forward paper trading, and periodic stress tests.
- Change control: who can alter code/parameters, how changes are reviewed and logged.
- CCO oversight: a CCO who understands the models and can suspend them when needed.
- Annual review of effectiveness, incidents, and updates.
Form ADV Part 2A disclosures should include:
- What your algorithms do (plain English), limitations, data dependencies, and human oversight.
- Conflicts (e.g., product selection), fees by service (traditional vs algorithmic components).
- Performance presentation with proper disclaimers (no implication of future results).
Our discipline here is simple: if a regulator or client asked “Why did the system do X?”, we can show the rule, the input, the approval, and the timestamp—without digging through someone’s inbox.
Marketing Rule: how to talk about AI and algorithms without crossing the line
Under Rule 206(4)-1 (Marketing Rule), the safest path is to describe capabilities, not outcomes:
- ✅ “We use algorithms to monitor drift and propose tax-loss harvesting opportunities subject to human review.”
- ✅ “Quantitative models inform our investment process; all decisions are overseen by advisors.”
- ❌ “AI guarantees outperformance.”
- ❌ “Our algorithms predict market moves.”
Two habits keep us out of trouble: (1) avoid superlatives tied to performance, and (2) keep documentation of model limits, testing, and controls. When in doubt, we let compliance mark up the draft and we ship the conservative version.
Use cases and value metrics: what clients actually see on the dashboard
Clients don’t want to “see the algorithm.” They want to see what changed and why:
- Rebalance card: current vs target, drift %, estimated costs, and rationale (policy rule that fired).
- TLH card: realized losses YTD, remaining wash-sale windows, surrogates used.
- Risk card: VaR/CVaR levels vs policy bands, correlation heatmap highlights, and any circuit breaker status.
- Attribution card: factor and sector contributions for the period.
In practice, this quietly sells the proposition. Our Miami HNW clients often bring spouses or partners to reviews because the dashboard turns complexity into a shared language. The tone stays institutional: process, not promises.
Algorithmic Trading Infrastructure & Platforms: APIs, ProRealTime, MT4 — When They Fit and When They Don’t
Pick tools based on controls, connectivity, and auditability, not novelty:
- Broker APIs / Direct Indexing for granular TLH and constraints.
- Execution algos + SOR for cost and market-impact control.
- ProRealTime/MT4/others: useful for certain workflows, but ensure you can export logs, tag orders to clients/accounts, and integrate with your compliance archive.
Our non-negotiables: role-based access, change logs, data lineage, and exportable evidence. If a platform can’t answer “who changed what, when, and why,” it doesn’t touch client assets.
Algorithmic trading for RIAs — FAQs
See What Our Clients Are Saying
How do you ensure algorithms don’t act without human oversight?
We run a hybrid model: algorithms monitor and propose; advisors approve material changes. All actions carry an audit trail (rule triggered, data used, timestamp, approver). Circuit breakers pause trading on data anomalies or extreme spreads.
Can you do this compliantly with the SEC and avoid “AI hype” issues?
Yes. We operate under Rule 206(4)-7 with written policies, model testing, and change control. Marketing follows Rule 206(4)-1: we describe capabilities, not performance promises; we disclose limitations and keep humans in the loop.
What real value do clients see beyond buzzwords?
Quiet, measurable wins: tighter drift control, systematic tax-loss harvesting (with tax budgets), and transparent execution. Dashboards show “what changed and why,” not black-box charts.
How do you prevent wash-sale violations during TLH?
The system checks holding periods and open windows, then uses pre-approved surrogate lists to maintain factor intent. Every TLH trade logs rationale, expected benefit, and next eligible dates.
What if the model underperforms for a period?
We treat it as model risk: publish assumptions, monitor factor regimes, cap turnover, and review annually. We never promise outperformance; we optimize consistency, tax outcomes, and process control.
How do you pick execution algos (VWAP/TWAP/POV) for best execution?
By liquidity, urgency, and event risk. A decision rule selects VWAP for steady high-liquidity flows, TWAP for thin tapes, POV for variable volume—each with caps, venue filters, and real-time spread guards.
What does onboarding look like—and how fast do clients see benefits?
Weeks, not months. Step 1: IPS mapping and constraints. Step 2: data validation and paper-testing. Step 3: staged go-live with tighter monitoring initially. Clients see drift control and TLH opportunities in the first review cycle.
How is data quality handled so models don’t go off the rails?
Multiple vendors + sanity checks (stale price detection, corporate-action alignment, outlier filters). If inputs fail QC, circuit breakers halt non-urgent orders and alert the team.
Will this lock me into a single platform or custodian?
No. We favor open APIs, exportable logs, and broker/custodian neutrality. Non-negotiables: role-based access, change logs, evidence exports. If a tool can’t answer “who changed what, when, and why,” we don’t use it.
How are fees structured and justified?
Transparent AUM fee, same fiduciary standard. The algorithmic layer reduces manual drag (rebalance discipline), surfaces TLH systematically, and improves documentation—value you can see in the dashboard and in year-end reports.
Algorithmic trading, when implemented as policy automation with human oversight, fits the fiduciary mandate: more consistent portfolios, better documentation, and clearer conversations. In my experience, the win isn’t just efficiency; it’s credibility—with clients, with committees, and with regulators.