Academy notes
Articles and updates from SoloQuant Academy.
Feng, He & Polson (2018) study deep nets for predicting asset returns from characteristics. Plain-language intro. Not investment advice.
Justin Sirignano (2016) applies spatial neural nets to limit-order-book states—an early LOB deep-learning baseline. Plain-language intro. Not investment advice.
Guijarro-Ordonez, Pelger & Zanotti (2021) use deep nets to extract residual spreads for statistical arbitrage. Plain-language intro. Not investment advice.
Bühler, Gonon, Teichmann & Wood (2018) train neural nets to hedge derivatives under frictions instead of relying on Black–Scholes deltas. Plain-language intro. Not investment advice.
Dixon, Klabjan & Bang (2017) recast market prediction as classification with deep neural nets instead of return regression. Plain-language intro. Not investment advice.
Install Langflow, build one visual LangChain-style flow, and know when to prefer it over Flowise after work.
Run Flowise, drag a tiny LLM/agent flow, and learn visual agent building for after-work beginners.
Install or open n8n, build one AI Agent workflow for reports or alerts, and automate beyond chat after work.
Spin up Dify, build one low-code LLM app or agent, and ship an internal tool after work without heavy infra.
Install OpenClaw from openclaw.ai, connect one chat channel, and run a personal coding/agent harness you can message after work.
Install Claude Desktop, enable one connector/MCP server, and call a local tool from chat—no CLI required tonight.
Install FastMCP from gofastmcp.com, expose one Python function as an MCP tool, and run it locally after work.
