KyoVest modernizes the age-old concept of community investing for the digital age, using smart contracts to create protected trading vaults where experienced investors and newcomers collaborate for mutual benefit. Our platform combines the best aspects of copy trading, DAOs, and traditional investment funds:- Experienced investors enhance returns by protecting and guiding newer participants- New investors get downside protection while maintaining trade-by-trade capital control- All participants benefit from shared profit distribution based on contribution and timingInstead of the "winner-takes-all" or "musical chairs" exits seen in traditional trading, we create positive-sum outcomes through:- Smart contract protection mechanics shielding newer investors from losses- Profit-sharing incentives aligning experienced and novice investors- DAO-like collective decision making with individual capital control- Fair profit distribution based on contribution and time investedThis solves critical problems by:- Eliminating "exit liquidity" and cascade losses in volatile markets- Providing greater control than traditional fund structures- Creating genuine alignment between all participants

Market forecasting for crypto, currencies, and equities, using machine-learning. • Created a system to predict price and volatility for financial time series. Usable for market forecasting, portfolio allocation, options-pricing, anomaly detection, and trading. Employs ARIMA and GARCH model ensembles, proprietary dimensionality-reduction and population-clustering techniques, quasi-monte-carlo resampling and cross-validation, and classical trade backtesting and simulation methods. • Built a self-updating data-warehouse to collect raw financial data from provider APIs and apply an ETL process to enable easy access for research, conversion into trading signals, with processes to self-calibrate trading models and deliver model signals to email, Twitter, and vendor APIs like QuantConnect and MetaTrader for automated trading. • Tech Stack: Python 3 (pandas/numpy/scikit-learn/Cython), MySQL, R, Ubuntu Linux

Online US tax-filing service. Revived the business by increasing margins from 3% to 30% with no customer loss through competitive pricing analysis, financial forecasting, and fixing technical debt. C#, ASP.NET, MS SQL.

Exchange-traded-fund and equity financial analytics SaaS, targeting investment advisers. • Created an ETL system to automatically collect raw securities data from provider APIs like Plaid, Ultumus and Redfin, and transform them into an intermediate layer to deliver on-demand analytics for over 250k securities to any custom user interface – an 83x capacity improvement over the company’s legacy system. Public demo at: https://app.logicly.finance/overview?view=general&secid=549535 • Prototyped a system to classify ETFs into investor-interest categories using a combination of nomenclature heuristics, dictionary-based natural language processing methods, asset-holdings comparisons, and financial statistics. Used to identify groups of similar ETFs in order to determine best-and-worst of breed on various criteria, so that financial advisors can suggest alternatives to their clients. • Tech Stack: Python 3 (flask/airflow/nameko), PostgreSQL, Amazon Web Services (AWS), Linux

Bloomberg-style service for analyzing trading profitability on the DESO web3 blockchain. PySpark, PostgreSQL.

Proprietary market-making for currencies/foreign exchange and futures. • Coordinated technical change process for over 170 clients over 30 software implementations between customers, sales team, engineers, and traders, cutting average go-live times from 3-weeks to 3-days. • Created an automated insights platform to sub-slice the firm’s trading behavior into discrete groups by client, currency, and other factors, delivered periodically through emailed Excel spreadsheets and live via web dashboard. Used by sales team to increase trading revenue by over $5MM/year, by easily identifying customer behavior patterns and anomalies for cross-selling and error-remedying. • Created a live-trading and backtesting/research system for monitoring and calibrating tick-based futures-trading strategies with support for profitability visualization and rolling-window walk-forward and genetic performance optimization, at a 30x speed improvement versus the company’s legacy system. Used by Windows-based traders to connect to the firm’s Linux-based trading and risk management layer. • Tech Stack (Linux): Python 2/3 (flask, django), MySQL, MongoDB, Javascript (bootstrap.js), R Tech Stack (Windows): C#, .NET (DevExpress, QuickFix, Windows Forms), HDF5

Proprietary high-frequency equities trading. • Built an ETL system to warehouse cold Nanex tick data archives and convert them into a hot OneTick database for random access. Used by quants to do targeted research on specific securities and timeframes. • Built a monitoring system to process intraday trading activity and compute profitability in real-time. Used by traders to control their automated trading models. • Built R-based scripts to automatically gather and summarize end-of-day and periodic performance across trader accounts. Used by management to assess profitability and manage risk. • Tech Stack: C#, .NET (DevExpress, Windows Forms, NUnit), Nanex, OneTick, MySQL, R

Proprietary medium-frequency systematic equities trading. C#.

Formerly MC Trading. Becomes Endura Group in 2008. Medium-frequency systematic equities trading. C#.

MC Trading was a discretionary trading company that employed tape-reading to trade using data from the specialists on the floor of the NYSE.

Proprietary high-frequency futures trading.