Johnny·PraxeoSys

Johnny — building PraxeoSys

Deconstruct markets through human actions, deploy code to sovereign runtimes, and extract yield from liquidity rifts.

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The Loop

Four identities, one process. Not parallel labels — sequential stages of the same cognitive loop.

Start from an economic reading of the market — a mispricing, a structural shift, a behavioral pattern. TODO: link to the research post that documents your current thesis.

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Proof

No claim on this site is unbacked. This section is the evidence.

GitHub activity
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Projects

Shipped, demoable work only.

01

Hyperliquid BTC Bot

A rules-based BTC perpetual-futures swing system with hard circuit breakers, running on a $1,000 proof-of-concept stake.

Problem
Discretionary crypto trading doesn't scale and leaves no audit trail; going systematic needs a strategy that's honest about what it can't predict, plus execution discipline no human sustains alone.
Method
Built on Freqtrade with a Smart Money Concepts strategy layer and a Coinglass sidecar for order-flow data (funding, open interest, liquidation levels). Three independent circuit breakers sit in the execution loop; positions pyramid in on confirmation with a ratcheting stop, and take-profit scales out via a 5-factor structural-reversal score.
Result
5 execution-layer bugs were caught only by live dry-run testing (not code review), including 2 that silently broke risk-budget math — all fixed. A pre-registered study of 15 crash-precursor indicators honestly reported 0 as reliably predictive rather than tuning the method for a nicer result.
FreqtradePythonDockerCoinglass API
02

Trade Agent

An AI pipeline that finds, scores, and drafts multilingual outreach to B2B export leads.

Problem
Manually searching overseas company directories, writing cold emails one by one, and tracking follow-ups by hand doesn't scale.
Method
A Python pipeline: Serper Maps API for lead discovery, a scraper + DeepSeek scoring pass for enrichment, AI-drafted emails/WhatsApp/LinkedIn messages in the target market's language, WeChat-notified human approval, and an autopilot mode that auto-sends high-confidence drafts while routing uncertain ones to a person. Deployed with Docker on a small VPS.
Result
Processed 192 leads end-to-end at an average AI quality score of 6.37/10, for roughly $0.006 in API cost per lead. Target metrics track cold-email industry benchmarks: <1.5% bounce rate, 5–10% reply rate (vs. 2–5% industry norm), <$10 cost per qualified inquiry (vs. $5–50 norm).
PythonFastAPIDeepSeek APISerper APIPlaywrightDockerSQLite
03

TideHook

A WeChat mini-program marketplace matching sea-fishing charter boats with anglers.

Problem
Sea-fishing charters get booked by phone and word-of-mouth — no way to compare boats or prices, or read real reviews, before paying.
Method
Built the full stack solo: UniApp/Vue3 mini-program frontend, Supabase backend with row-level security for zero-trust data isolation, three user roles (anglers/boat owners/fishing guides), in-app chat with read receipts, and an AI fish-ID feature backed by a 60-species field guide.
Result
10+ core modules shipped end-to-end across all three roles; real-name auth and in-app payments are fully spec'd and ready, blocked only on the business license needed to apply for a WeChat Pay merchant account.
UniAppVue 3SupabasePostgreSQL RLSWeChat Mini Program
04

LinguaNest

An Android app that simulates a bilingual home for kids whose parents don't speak English.

Problem
Chinese parents who don't speak English can't create an immersive bilingual environment for their kids, and most English-learning apps are one-way content feeds with no real interaction.
Method
An on-device audio pipeline: voice-activity detection listens for the parent's Chinese speech, triggers TTS of the matching English word, then opens an 8-second window listening for the child's attempt at repeating it in English, matched via edit-distance against a variant table. A correct match triggers a haptic buzz and a full-screen bilingual translation card coaching the parent's response.
Result
The word-matching engine tested at 22 true positives / 0 false positives (100% hit rate, 0% false-positive rate). Both directions of the voice loop are wired end-to-end; real-device acceptance testing is the one item left before the next milestone.
FlutterDartOn-device ASR/TTS/VADDrift (SQLite)
05

Markdown Knowledge Base

A GitHub-native, all-Markdown personal knowledge base where Claude Code is the archivist.

Problem
Personal knowledge (AI chat exports, articles, books, video transcripts) scatters across tools with no durable, searchable, vendor-independent home — and manual organizing rarely survives contact with real volume.
Method
A zero-custom-tooling pipeline: off-the-shelf converters normalize any source into Markdown and drop it in inbox/, then Claude Code acts as the archivist — deduping by source ID, routing into a typed directory structure, backfilling frontmatter, building wikilinks, and updating Dataview-driven indexes — all governed by a written rulebook rather than ad-hoc judgment calls.
Result
Supports 4 source types (AI session exports, web articles, books/long docs, video transcripts) end-to-end; extending to a new source takes exactly 2 steps (one converter script, one routing-table row) with zero changes to the core structure. Sensitive-data scanning runs twice — once in the archiving pass, once via a GitHub Actions gitleaks check on push — before anything reaches the remote.
MarkdownObsidianClaude CodeGitHub ActionsDataviewgitleaks

Research

A digital garden of theses, market structure notes, and honest post-mortems.

View all research →

Now

What I'm focused on, right now.

Focus
TODO: what you are focused on right now (e.g. "Rebuilding a mean-reversion signal for BTC perp funding")
Researching
TODO: the open question you are currently researching
Watching
TODO: the market opportunity you are watching and why

Last updated: Jul 16, 2026

Contact

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