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Igor Ganapolsky
Igor Ganapolsky

Posted on • Edited on • Originally published at igorganapolsky.github.io

Daily Report: February 19, 2026 | $-29.60

#dailyreport #aitrading #riskmanagement #options

AI Disclosure: This daily report was generated by our AI trading system. All financial
data comes from live sources: Alpaca (broker), FRED (Treasury yields), and our RAG system
(lessons learned). Every number is verifiable. Human oversight: Igor Ganapolsky.

Learning Day: Thursday, February 19, 2026

Day 114/90 of our AI Trading R&D Phase

TL;DR: Daily P/L $-29.60 (-0.03%), portfolio $101,366.28, trades: 16.

Executive Summary

Metric Value
Daily P/L $-29.60 (-0.03%)
Total P/L $+1,366.28 (1.37%)
Portfolio Value $101,366.28
Cash $101,366.28
Buying Power $202,732.56

Today's Trades

Symbol Action Qty Price P/L
SPY260327P00645000 SELL 1.00 $5.34 N/A
SPY260327P00640000 SELL 1.00 $4.75 N/A
SPY260327C00730000 SELL 1.00 $0.46 N/A
SPY260327C00725000 BUY 1.00 $0.77 N/A
SPY260320C00720000 BUY 1.00 $0.81 N/A
SPY260320P00650000 BUY 1.00 $4.97 N/A
SPY260327P00650000 BUY 1.00 $6.04 N/A
SPY260327P00655000 BUY 1.00 $6.80 N/A
SPY260327P00645000 sell 1.00 $5.34 N/A
SPY260327P00640000 sell 1.00 $4.75 N/A

Portfolio Allocation

Our current strategy focuses on:

  • US Equities: SPY, sector ETFs
  • Options: Cash-secured puts, covered calls
  • Fixed Income: Treasury ETFs (SHY, IEF, TLT)

Treasury & Fixed Income

Live Treasury Yields (FRED API):

Maturity Yield
2-Year 3.43%
5-Year 3.63%
10-Year 4.05%
30-Year 4.68%

Yield Curve Spread (10Y-2Y): +0.62%

Curve Status: Normal (positive slope)

Data source: Federal Reserve Economic Data (FRED) API

Risk Metrics

  • Max Position Size: 2% of portfolio (Kelly Criterion)
  • Stop Loss: Volatility-adjusted per position
  • Circuit Breakers: Active (no triggers today)

Backtesting & Risk-Adjusted Returns

Sharpe Ratio Analysis

The Sharpe Ratio measures risk-adjusted return: how much excess return we get per unit of risk.

Metric Value Interpretation
Sharpe Ratio 0.00 Negative (strategy losing money)
Sortino Ratio 0.00 Downside risk-adjusted
Profit Factor 0.00 Gross profit / Gross loss
Max Drawdown 0.0% Worst peak-to-trough decline

Backtest Performance

Metric Value
Total Trades 18
Win Rate 100.0%
Strategy Iron Condors on SPY

Our Backtesting Methodology

  1. Historical Data: We use Alpaca's historical options data with realistic IV estimation
  2. Black-Scholes Pricing: Options priced using Black-Scholes with rolling historical volatility
  3. Slippage & Costs: 0-5% slippage built into simulation
  4. Exit Rules: 50% profit target, 200% stop loss, or 7 DTE exit (per LL-268)

Strategy: Iron Condors on SPY

Our strategy sells both put spreads and call spreads on SPY:

Bull Put Spread (downside protection)
  └── Sell 15-delta put
  └── Buy 20-delta put ($5 wide)

Bear Call Spread (upside protection)
  └── Sell 15-delta call
  └── Buy 20-delta call ($5 wide)
Enter fullscreen mode Exit fullscreen mode

Why Iron Condors?

  • Collect premium from BOTH sides
  • 15-delta = ~85% probability of profit
  • Defined risk on both directions
  • Profit when SPY stays within range

Risk Management:

  • Max 5% of capital per trade ($248 on $5K account)
  • Stop loss at 200% of credit received
  • Close at 7 DTE to avoid gamma risk (LL-268: improves win rate to 80%+)

Sharpe ratio calculated using annualized returns with 4.5% risk-free rate (current 3-month T-bill).

Tech Stack in Action

Today's trading decisions were powered by our AI stack:

flowchart LR
subgraph Today["Today's Pipeline"]
DATA["Market Data
(Alpaca)"] --> GATES["Gate Pipeline"]
GATES --> CLAUDE["Claude Opus 4.5
(Risk Decision)"]
GATES --> RAG["LanceDB RAG
(Past Lessons)"]
CLAUDE --> EXEC["Trade Execution"]
RAG --> CLAUDE
end

Technologies Used Today

Component Technology Role
Decision Engine Claude Opus 4.5 Final trade approval, risk assessment
Cost-Optimized LLM OpenRouter (DeepSeek/Kimi) Sentiment analysis, market research
Knowledge Base LanceDB RAG Query lessons learned
Retrieval LanceDB Semantic search over trade history
Broker Alpaca API Paper trading execution
Data FRED API Treasury yields, macro indicators

How It Works

  1. Market Data Ingestion: Alpaca streams real-time quotes and positions
  2. Gate Pipeline: Sequential checks (Momentum → Sentiment → Risk)
  3. RAG Query: System retrieves similar past trades and lessons
  4. Claude Decision: Final approval with full context (86% accuracy)
  5. Execution: Order submitted to Alpaca if all gates pass

Full Tech Stack Documentation

Alpaca Snapshot + PaperBanana Technical Narrative

Paper Account

Alpaca Snapshot PaperBanana Financial Diagram
Paper Account Snapshot Paper Account PaperBanana Diagram

Captured: 2026-02-19T17:35:53Z

Technical interpretation: Paper Account: net liquidation value $101,356.56; daily P/L -58.28 (-5.7 bps) indicating a negative drift session; cumulative P/L +1,356.56 (+1.36%); low capital deployment at 2.2% utilization with cash $101,644.56; open position proxy 5; win-rate estimate 100.0% (n=1); North Star gate MEDIUM.

Brokerage Account

Alpaca Snapshot PaperBanana Financial Diagram
Brokerage Account Snapshot Brokerage Account PaperBanana Diagram

Captured: 2026-02-19T17:35:53Z

Technical interpretation: Brokerage Account: net liquidation value $208.03; daily P/L +0.00 (+0.0 bps) indicating a flat premium-decay session; cumulative P/L +188.03 (+940.15%); high capital deployment at 90.4% utilization with cash $40.00; open position proxy 0; win-rate estimate 0.0% (n=0); North Star gate MEDIUM.

Market Context

US equity markets trade Monday-Friday, 9:30 AM - 4:00 PM ET.

What's Next

Day 115 focus:

  • Continue systematic strategy execution
  • Monitor open positions
  • Refine ML signals based on today's data

FAQ

What was today's P/L?

$-29.60 (-0.03%).

What is the current portfolio value?

$101,366.28.

Where is the canonical version of this report?

This post's canonical URL is https://igorganapolsky.github.io/trading/reports/2026-02-19-daily-report/

View Source

About This Post

How this post was created:

  • 🤖 AI Generated: Content structured by Claude Opus 4.5
  • 📊 Data Sources: Alpaca API (trades), FRED API (yields), RAG (lessons)
  • ✅ Data Verification: All numbers pulled from live APIs, no fabrication
  • 👤 Human Oversight: Igor Ganapolsky reviews publication workflow
  • 📅 Generated: 2026-02-19 14:22 ET

Per WordPress AI Guidelines: We clearly disclose
AI involvement, verify all data, and maintain human oversight.

Not financial advice. Paper trading only.

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