An investing research platform built on the belief that elite portfolio management should be available to everyone.
Founded by George Dikos. Powered by independent research and AI.
The edge is process, not information
The tools used by the world’s best investors — systematic strategy testing, ensemble portfolio construction, AI-driven risk management — have never been inaccessible because of talent.
The cost and complexity barrier
They have been inaccessible because of cost and complexity.
Research and agentic AI close the gap
Goldflower’s thesis is that independent research coupled with agentic AI can make elite portfolio management available to the everyday investor — and exists to drastically remove these barriers.
Every day, the platform tests thousands of strategy configurations against decades of market data, identifies the winning ones, and builds a portfolio from their consensus. No single strategy. No gut feel. A living ensemble that updates daily.
The full methodology, backtesting framework, and statistical validation are documented in the research paper. Read the research →
Strategy universe
Thousands of configurations across lookback, holding period, portfolio size and benchmark.
Pick the winners
Ranked point-in-time on data available that day. No future information, ever.
Pool and weight
Holdings from every winner are pooled; the better an asset ranks, the more weight it gets.
Execute at T+1
Signals trade at the next day’s close, so nothing can act on information it did not have.
1. Strategy universe
The engine enumerates strategy configurations across multiple dimensions: lookback windows (5 to 100 days), holding periods (1 to 20 days), portfolio sizes (5 to 30 assets), and benchmark indices. Each configuration produces a daily list of top-ranked assets based on historical performance patterns stored in a PostgreSQL database with 5+ years of market data.
2. Winning strategy selection (point-in-time)
At each evaluation date T, strategies are ranked by cumulative performance up to and including T — never using future information. Point-in-time SQL queries ensure strict temporal integrity. The top strategies by streak return and average forward 5-day return are selected.
= 6 winning strategies per evaluation date
Minimum 5 observations required per strategy
3. Asset pooling and rank-based weighting
Holdings from all winning strategies are pooled. Each asset receives a weight inversely proportional to its best rank across all strategies in which it appears.
Rank 1 (best) → highest weight
Assets appearing in multiple strategies use their best (lowest) rank
4. T+1 execution
All signals are executed at the next trading day’s close price. This strict rule eliminates lookahead bias entirely. The portfolio cannot trade on the same day a signal is observed.
Return measured from T+1 close to holding period end
5. Performance measurement
Reported metrics: cumulative return, annualized return, annualized volatility, Sharpe ratio (risk-free rate 4.75%), maximum drawdown, and annualized alpha versus the S&P 500. All results use real historical prices from Yahoo Finance. Transaction costs are estimated separately.
Better portfolios and better risk management through the combination of systematic research and agentic AI — available to everyone, transparent, and improving every day.
Daily Ensemble Portfolio
Every morning, the engine distills thousands of backtested strategy combinations into a single high-conviction portfolio, updated daily and delivered before the market opens.
Agentic Risk Management
An AI layer screens holdings, detects regime shifts, weatherproofs portfolios, and helps execute — moving from recommendation to action.
A Learning System
Every recommendation is tracked. Every outcome measured. The goal is a system that gets measurably better every day — eventually managing portfolios autonomously, with full transparency.
Everyone Gets the Same Engine
No tiers, no better answer for a larger balance. The person with a first thousand invested sees exactly what everyone else sees, at the same time each morning.
I design, fund, and build Goldflower. My background is in structural econometrics, operational research, and applied machine learning. I was the first research scientist at Amazon EU, and before that I built integrated planning models for industrial operations at Heracles/Lafarge.
The idea started in 2014 after Tucker Balch’s quantitative investing course on Coursera and reading More Money than God. I started trading warrants of Greek banks (long) versus the underlying shares (short), proving to myself that long-short strategies could generate alpha with less volatility — and that this contradicted the efficient market hypothesis I’d studied at MIT. From that point I kept adding strategy types, introduced a pruning approach to shortlist the ones that worked, and configured portfolios from the winners. That system became EventSim, and four years of building it became Goldflower.
The name comes from my family. Like Greek ship owners who name their ships after people they love, I wanted to signal how personal this is. Goldflower is named after Chrysanthi — the golden flower.
AWS EC2 + PostgreSQL
Strategy engine and data store
Strategy Engine
Thousands of daily configurations · Python + yfinance
Daily Portfolio
Ensemble of winning strategies · AWS S3
Delivery
Daily email before market open · AWS Lambda + SES
Execution
Paper and live trading · IB Gateway
Intelligence
Screening, risk, conversation · Claude AI Agent
Past performance is not indicative of future results. All portfolio strategies involve risk, including the potential loss of principal. Goldflower does not provide personalized investment advice. The information provided is for educational and research purposes only and does not constitute a solicitation or offer to buy or sell securities. Sharpe ratios and returns shown are backtested results. Backtest assumptions: zero transaction costs, T+1 entry at previous close × 1.001 limit, daily close stop monitoring. Live performance may differ materially from backtested results.