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  <description>Guides to algorithmic trading and prediction-market research.</description>
  <item>
    <title>What is algorithmic trading? A plain-English guide</title>
    <link>https://jordansheets.ai/learn/what-is-algorithmic-trading/</link>
    <guid>https://jordansheets.ai/learn/what-is-algorithmic-trading/</guid>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
    <description>Algorithmic trading means making trading decisions with explicit, testable rules. Here is how it works, the main strategy families, and how to start researching without writing code.</description>
  </item>
  <item>
    <title>How to backtest a trading strategy without fooling yourself</title>
    <link>https://jordansheets.ai/learn/how-to-backtest-a-trading-strategy/</link>
    <guid>https://jordansheets.ai/learn/how-to-backtest-a-trading-strategy/</guid>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
    <description>A step-by-step backtesting method, the six biases that make bad strategies look good, and the spreadsheet layout and formulas to run one honestly.</description>
  </item>
  <item>
    <title>How prediction market prices work, and how to read them as probabilities</title>
    <link>https://jordansheets.ai/learn/prediction-market-prices-as-probabilities/</link>
    <guid>https://jordansheets.ai/learn/prediction-market-prices-as-probabilities/</guid>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
    <description>A Kalshi contract that pays $1 trading at 62¢ implies about a 62% chance. Here is how bids, asks and multi-outcome markets turn into clean implied probabilities, with formulas.</description>
  </item>
  <item>
    <title>Market calibration: do favorites win as often as their price says?</title>
    <link>https://jordansheets.ai/learn/market-calibration/</link>
    <guid>https://jordansheets.ai/learn/market-calibration/</guid>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
    <description>How to test whether prediction-market prices are well calibrated, with price bins, a reliability chart, the Brier score and the sample-size check most studies skip.</description>
  </item>
  <item>
    <title>Expected value and edge: the math behind every profitable trade</title>
    <link>https://jordansheets.ai/learn/expected-value-and-edge/</link>
    <guid>https://jordansheets.ai/learn/expected-value-and-edge/</guid>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
    <description>How to compute the expected value of a binary event contract, include fees and spreads, find your breakeven probability, and tell edge apart from variance.</description>
  </item>
  <item>
    <title>Position sizing with the Kelly criterion (and why most traders use a fraction of it)</title>
    <link>https://jordansheets.ai/learn/kelly-criterion-position-sizing/</link>
    <guid>https://jordansheets.ai/learn/kelly-criterion-position-sizing/</guid>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
    <description>The Kelly criterion tells you what fraction of your bankroll to risk on a bet with an edge. Here is the formula for binary event contracts, a worked example, and why fractional Kelly is the practical choice.</description>
  </item>
  <item>
    <title>How to research trades with an AI spreadsheet agent</title>
    <link>https://jordansheets.ai/learn/ai-spreadsheet-agent-for-trading-research/</link>
    <guid>https://jordansheets.ai/learn/ai-spreadsheet-agent-for-trading-research/</guid>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
    <description>A practical guide to using an AI agent that builds trading research in a spreadsheet, covering how to phrase questions, review changes, and keep the analysis auditable.</description>
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