Almost Surely Profitable
An ongoing experiment: can an LLM, given the right behavioral priors, make non-trivial portfolio allocation decisions?
The setup is simple. Every day after US market close, a Python pipeline fetches data for 30 assets (ETFs, small caps, commodities, French equities), computes technical indicators, and hands everything to a large language model. The LLM’s system prompt is injected with risk management principles from prospect theory and CVaR – making it loss-averse by design, not by accident.
The LLM returns a JSON object: buy, sell, or hold for each asset, with position sizing. Python executes the paper orders and logs everything.
Starting capital: EUR 10,000. No real money. No broker. Just math, markets, and a hypothesis.
Repository: almost-surely-profitable
Open Positions
| Asset | Qty | Avg Price | Current | Value | P&L |
|---|---|---|---|---|---|
| SPY | 2.652319458001969 | $746.6356999909071 | $739.02001953125 | €1960.12 | €0 (0%) |
| FEZ | 18.735126891665615 | $68.53729858712835 | $68.16999816894531 | €1277.17 | €0 (0%) |
| GLD | 2.886276213231957 | $378.150480872856 | $374.55999755859375 | €1081.08 | €0 (0%) |
| IJR | 6.0421630052117274 | $146.80999755859375 | $145.5 | €879.13 | €0 (0%) |
| TLT | 6.487549530231055 | $83.97000122070312 | $83.76000213623047 | €543.4 | €0 (0%) |
| AI.PA | 2.84459639404416 | $177.17999267578125 | $177.17999267578125 | €504.01 | €0 (0%) |
| REET | 16.59067048400136 | $27.90999984741211 | $28.795000076293945 | €477.73 | €0 (0%) |
| SAN.PA | 4.909103512711255 | $73.07906183264495 | $78.08000183105469 | €383.3 | €0 (0%) |
| DBA | 9.314385188904772 | $26.690000534057617 | $27.610000610351562 | €257.17 | €0 (0%) |
| Week | Starting Value | Ending Value | Return | vs SPY B&H | vs CAC B&H |
|---|---|---|---|---|---|
| Week 1 | €10,000.00 | €10,004.86 | +0.05% | pending | pending |
Daily Trade Log
2026-02-19 — Portfolio Construction Day
Portfolio Value: €10,004.86 (+0.05% since inception)
Cash Deployed: €4,254.56 (5 new positions)
Cash Remaining: €3,745.44 (37.4% buffer)
| Action | Asset | Allocation | Price | Rationale |
|---|---|---|---|---|
| BUY | QQQ | 20% | $603.47 | Mean reversion play. RSI at 34.5 (oversold), Bollinger position 0.25 (near lower band). Tech sector has sold off; this is a disciplined entry on weakness, not strength. |
| BUY | GLD | 15% | $459.56 | Diversification. Gold maintains ~30% correlation with SPY during risk-off periods. Acts as portfolio insurance without the theta decay of options. |
| BUY | MC.PA | 10% | €531.50 | Deep value in European luxury. RSI 40.1, -18.5% drawdown from highs. LVMH trades at a discount to historical multiples despite resilient fundamentals. Sized at 10% to limit single-stock concentration risk. |
| BUY | TLT | 15% | $89.62 | Negative correlation (-0.45) with SPY for downside protection. Rates have repriced higher; duration risk is now more symmetric. Bonds provide convexity that equities lack. |
| BUY | DBA | 10% | $25.90 | Agriculture as uncorrelated alternative. 5.2% realized volatility, near-zero correlation with equity indices. Soft commodities have different macro drivers than financial assets. |
| HOLD | SPY | 20% | — | Existing position from 2026-02-18. Small unrealized gain (+0.24%). Continue holding as core equity exposure. |
What We Avoided (and Why):
- SLV — 144% annualized volatility. The tail risk exceeds our CVaR tolerance.
- AIR.PA — Down 6.75% in one session. Momentum is negative; catching falling knives violates loss aversion.
- TTE.PA / ^FCHI — RSI 82.3 and 78.9 respectively. Overbought conditions increase probability of mean reversion against us.
Portfolio Characteristics After Rebalancing:
- 6 positions across 4 asset classes (US equity, gold, bonds, agriculture, European luxury)
- 37.4% cash buffer for drawdown resilience and opportunistic entries
- Weighted average correlation with SPY: ~0.65 (diversified but not exotic)
- No position exceeds 20% (concentration risk controlled)
Methodology
The agent operates under constraints inspired by behavioral economics:
- Loss aversion coefficient ~2.25x – the system prompt encodes Kahneman & Tversky’s asymmetry between gains and losses
- CVaR as primary risk measure – tail risk matters more than variance
- Maximum 25% allocation per position – no single-asset concentration
- Mandatory cash buffer – the agent keeps 10-30% in cash for opportunities and drawdown resilience
- Drawdown circuit breaker – if portfolio drops > 3% intraday, the system triggers an alert
The theoretical foundation comes from Behavioral_RL, which applies prospect theory and CVaR to reinforcement learning on the Iowa Gambling Task. The key insight: encoding human cognitive biases into the decision framework can sometimes improve risk-adjusted returns compared to “perfectly rational” agents, because it prevents the kind of tail-risk exposure that wipes out portfolios in practice.
Universe
ETFs: SPY (S&P 500), QQQ (Nasdaq 100), GLD (Gold), TLT (US Bonds), FEZ (Euro Stoxx 50), CAC 40
Small Caps: IWM (Russell 2000), IJR (S&P Small-Cap 600), VB (Vanguard Small-Cap), GWX (International Small Cap)
Commodities: SLV (Silver), USO (Oil), DBA (Agriculture), PDBC (Diversified Commodities), COPX (Copper Miners)
Euronext Paris: LVMH, TotalEnergies, Sanofi, L’Oreal, Airbus, Schneider Electric, Air Liquide, BNP Paribas, AXA, Hermes, Safran, Dassault Systemes, Vinci, Saint-Gobain, Kering
30 assets. Three continents. Multiple asset classes. One stochastic process with an unknown distribution. The universe is defined in config/universe.json and evolves as the agent discovers new opportunities.
Disclaimer
This is research. Paper trading only. No financial advice. The name “Almost Surely Profitable” is a probability theory joke, not a guarantee. If markets were predictable with probability 1, I’d be writing this from a yacht, not a terminal.
Updated daily after US market close. Last update: February 19, 2026