Markets reward those who understand how to think under uncertainty.
Macropoiesis teaches the quantitative methods behind institutional analysis:
hypothesise, formalise, confront with data, revise.
Scientific method as investable skill.
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Macropoiesis is not a collection of standalone tools. The explicit long-term goal is to construct an underlying generative model of the financial system — one that explains, predicts, and revises its own structure. The theoretical engine is the Free Energy Principle: every model, every indicator, every simulation on this platform is a component of a system that minimises surprise by continuously updating its beliefs against incoming evidence.
The system maintains a probabilistic world model of macroeconomic structure, market regimes, and inter-agent dynamics. When new evidence arrives — a policy shift, a liquidity event, a structural break — the model updates. The tools you learn here are the components of that system.
Ingest filings, macro data, network topology. Score observations against the current model's predictions.
Minimise free energy by adjusting priors — tighten distributions where the model is right, widen them where it's surprised.
Generate scenarios and counterfactuals that reduce expected uncertainty — the system seeks information, not confirmation.
Methodology sections with the formulas, a parameter→mechanism map, honest caveats — and experiments to try. The reasoning ships with the output.
Vary growth, discount rates, terminal growth, tail dependence. Sensitivity tornados, r×g heatmaps, scenario A/B comparison with overlaid distributions.
Rate shock, the terminal-value singularity, Jensen's inequality, tail dependence, Monte Carlo convergence — each runs live on real data and shows what moved, and why.
Prior beliefs, likelihood functions, posterior updating — the epistemic engine behind every tool on this platform.
Random sampling, convergence, variance reduction. Build a simulation engine step by step and understand why point estimates lie.
From deterministic spreadsheet to full probability distribution. Model assumptions as distributions, propagate uncertainty, interpret confidence bands.
Why RSI alone is incomplete. Integrating global M2 liquidity and news sentiment into a context-dependent signal.
Differential geometry meets systemic risk. Construct correlation networks, compute discrete Ricci curvature, and read structural fragility before it hits prices.
Apply geometric economics to pre-crisis banking data. Reconstruct the signals visible in network topology months before the collapse.
Connect MCP tools, structure a knowledge base, design retrieval pipelines, and build self-improving analytical loops.
Every tool and tutorial is grounded in formalised, testable methodology. We teach the reasoning — not the conclusion.
Members contribute analyses, discuss methods, and refine models together. Peer review, not guru culture.
Access to analytical methods should not be a privilege. No one is excluded by income.
Monte Carlo-simulated valuations with full uncertainty quantification. Input your own assumptions, see the distribution they imply.
RSI extended with global liquidity data (M2) and news sentiment — an indicator that understands context. Learn to read it, not follow it.
Ricci curvature on financial networks: detect structural fragility in correlation topology before it shows up in prices.
Community-contributed method discussions, shared models, and a growing knowledge base under open license.
Get notified when indicators shift, when network curvature crosses thresholds, or when new tutorials match your path.
Ask Claude to explain any Macropoiesis method, run a historical comparison, or walk through a tutorial step.
Claude reads your notes, saved analyses, and bookmarked research. Build a personal analytical knowledge base.
Connect external data sources, trigger simulations, and chain analytical steps through natural conversation.
"Science is a way of trying not to fool yourself — and you are the easiest person to fool."— Richard Feynman