Strategy Performance Report
tradesdontlie/tradingview-mcp
Builds a performance report for a backtested Pine Script strategy from TradingView data, covering metrics, trades, the equity curve and improvement ideas.
Analyzes crude oil, gold and copper through supply-demand balance, pricing models, inventory cycles and futures structure, and outputs directional signals suitable for backtesting.
Install Commodity Analysis Signals
$ npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install HKUDS/Vibe-Trading commodity-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/commodity-analysis .claude/skills/commodity-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "commodity-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/commodity-analysis into .claude/skills/commodity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "commodity-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/commodity-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install HKUDS/Vibe-Trading commodity-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .agents/skills && cp -r skills-src/agent/src/skills/commodity-analysis .agents/skills/commodity-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "commodity-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/commodity-analysis into .agents/skills/commodity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "commodity-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install HKUDS/Vibe-Trading commodity-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/agent/src/skills/commodity-analysis .cursor/skills/commodity-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "commodity-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/commodity-analysis into .cursor/skills/commodity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "commodity-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/HKUDS/Vibe-Trading.git --path agent/src/skills/commodity-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install HKUDS/Vibe-Trading commodity-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/agent/src/skills/commodity-analysis .gemini/skills/commodity-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "commodity-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/commodity-analysis into .gemini/skills/commodity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "commodity-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install HKUDS/Vibe-Trading commodity-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .github/skills && cp -r skills-src/agent/src/skills/commodity-analysis .github/skills/commodity-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "commodity-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/commodity-analysis into .github/skills/commodity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "commodity-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install HKUDS/Vibe-Trading commodity-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/agent/src/skills/commodity-analysis .opencode/skills/commodity-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "commodity-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/commodity-analysis into .opencode/skills/commodity-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "commodity-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
commodity-analysis- GitHub stars
- 35k
- Token cost
- ~2.3k tokens
- SKILL.md length
- 794 words
- Files
- 1
- Skills in repo
- 89
- Repo updated
- First seen
- Licence
- MIT
At a glance
Analyzes crude oil, gold and copper through supply-demand balance, pricing models, inventory cycles and futures structure, and outputs directional signals suitable for backtesting.
- Works in 6 steps: Crude Oil Supply-Demand Balance → Gold Pricing Framework → Dr. Copper as an Economic Predictor → …
- Judging whether the oil market is tight or loose from supply and demand data
- SKILL.md covers Overview, Core Concepts, Analysis Framework and Output Format, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
The skill examines commodities along four lines: supply-demand balance, a pricing model, the inventory cycle and futures curve structure, and it turns the result into directional signals that can be backtested. It concentrates on crude oil as the global pricing anchor, gold as a safe haven and inflation hedge, and copper as an economic barometer.
For oil it tabulates supply-side variables such as OPEC production, US shale output, rig counts and strategic reserve releases next to demand-side inputs like IEA forecasts, China imports, gasoline demand and global PMI. Gold uses a four-factor model weighted 40% real rates, 25% dollar index, 20% safe-haven demand and 15% central-bank buying, with rules of thumb for TIPS yields, and copper is treated as a leading indicator of industrial production. Seasonality is part of the stated scope.
When your agent uses it
- Judging whether the oil market is tight or loose from supply and demand data
- Assessing gold with real rates, the dollar index and safe-haven demand
- Using copper as a read on industrial activity
- Generating directional commodity signals for a backtest
Example prompts
- “Is the crude oil market tight right now? Walk through OPEC output, rig counts and inventories.”
- “Score gold with the four-factor model using the current 10Y TIPS yield and DXY.”
- “Use the copper to gold ratio to read where the economic cycle stands.”
- “Produce a directional oil signal that I can feed into a backtest.”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 7f6908b. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Commodity Analysis Signals loads about 2.3k tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 794 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from HKUDS/Vibe-Trading at commit 7f6908b, republished under its MIT licence (© HKUDS). 794 words, ~2,262 tokens.
Download SKILL.mdSave it as
.claude/skills/commodity-analysis/SKILL.md (or your agent's skills folder).- name
- commodity-analysis
- description
- Commodity analysis (oil supply-demand balance / gold pricing / copper as an economic predictor / inventory cycles / futures premium-discount structure / seasonality), generating directional commodity signals.
- category
- analysis
Commodity Analysis
Overview
Analyze commodities from four dimensions — supply-demand balance, pricing model, inventory cycle, and futures structure — and output directional signals suitable for backtesting. Focuses on crude oil (global pricing anchor), gold (safe haven + inflation hedge), and copper (economic barometer).
Core Concepts
1. Crude Oil Supply-Demand Balance
Key supply-side variables:
| Variable | Data Source | Frequency | Direction of Impact |
|---|---|---|---|
| OPEC production | OPEC monthly report | Monthly | Production cuts → oil price ↑ |
| US shale output | EIA weekly report | Weekly | Higher output → oil price ↓ |
| Rig count (Baker Hughes) | Baker Hughes | Weekly | Leads production by 3-6 months |
| Strategic Petroleum Reserve (SPR) | EIA | Weekly | SPR release → short-term oil price ↓ |
Key demand-side variables:
- IEA global oil demand forecast (quarterly)
- China crude imports (customs monthly data)
- US gasoline demand (EIA weekly report, implied demand)
- Global PMI (leads demand by 1-2 months)
Supply-demand balance signals:
# Simplified supply-demand judgment
if opec_compliance > 90% and us_rig_count_declining:
supply_signal = "tight" # bullish for oil
elif opec_compliance < 80% and us_production_rising:
supply_signal = "loose" # bearish for oil
if global_pmi > 50 and china_import_yoy > 5%:
demand_signal = "strong" # bullish for oil
elif global_pmi < 48 and china_import_yoy < 0:
demand_signal = "weak" # bearish for oil2. Gold Pricing Framework
Four-factor model:
| Factor | Weight | Logic | Indicator |
|---|---|---|---|
| Real rates | 40% | Real rates ↓ → lower opportunity cost of holding gold → gold ↑ | 10Y TIPS yield |
| US dollar index | 25% | USD ↓ → gold becomes cheaper in pricing terms → gold ↑ | DXY |
| Safe-haven demand | 20% | Risk ↑ → safe-haven buying → gold ↑ | VIX + geopolitical risk index |
| Central-bank buying | 15% | Central-bank purchases → structural demand support | WGC quarterly report |
Practical rules:
- 10Y TIPS < 0%: strong support for gold (negative real rates mean negative holding cost)
- 10Y TIPS > 2%: pressure on gold (positive real rates reduce attractiveness)
- Correlation between DXY and gold is around -0.6, but not absolute (they both rose in 2022 due to safe-haven demand)
- Central-bank purchases >1000 tons / year (2022-2023 level): long-term structural bullish support
3. Dr. Copper as an Economic Predictor
Copper as a leading indicator:
- YoY copper-price change leads industrial production by about 2-3 months
- Copper / gold ratio is highly positively correlated with the US 10Y Treasury yield (
r > 0.7) - Copper breakout above the prior high confirms economic recovery
Copper fundamental tracking:
| Indicator | Data Source | Threshold |
|---|---|---|
| LME copper inventory | LME daily report | <150k tons = tight |
| SHFE copper inventory | SHFE weekly report | WoW decline >10% = tight |
| Copper concentrate TC/RC | SMM | TC < $30/ton = tight mining supply |
| China copper imports | Customs monthly report | YoY growth >10% = strong demand |
4. Inventory Cycle Analysis
Visible inventory vs hidden inventory:
- Visible inventory: published by exchanges (LME / SHFE / COMEX), transparent and trackable
- Hidden inventory: bonded areas / trader warehouses, opaque but potentially larger
- The true turning point in prices is the turning point in total inventory
Four inventory-cycle stages (using copper as example):
Active restocking (price↑ volume↑) -> Passive restocking (price↓ volume↑) -> Active destocking (price↓ volume↓) -> Passive destocking (price↑ volume↓)
mid bull market late bull market mid bear market late bear / early bull marketSignal mapping:
| Stage | Inventory Direction | Price Direction | Trading Signal |
|---|---|---|---|
| Passive destocking | ↓ | ↑ | Long (best buying point) |
| Active restocking | ↑ | ↑ | Keep long positions |
| Passive restocking | ↑ | ↓ | Close longs (warning) |
| Active destocking | ↓ | ↓ | Short or stay neutral |
5. Futures Premium / Discount Structure
Contango (futures > spot, normal market):
- Supply is abundant, and the market prices in carrying costs (storage + funding)
- Roll yield is negative (
roll yield < 0), unfavorable for long holders - Deep contango (
far month - near month > 5%) = severe oversupply
Backwardation (futures < spot, inverted market):
- Supply is tight, and spot premium reflects strong immediate demand
- Roll yield is positive (
roll yield > 0), favorable for long holders - Deep backwardation (
near month - far month > 3%) = squeeze or extreme shortage
Term-structure signal:
# Spread ratio = (front month - second month) / front month
spread_ratio = (front_month - second_month) / front_month
if spread_ratio > 0.02: # backwardation > 2%
signal = "strongly bullish" # spot shortage
elif spread_ratio < -0.03: # contango > 3%
signal = "bearish" # oversupply
else:
signal = "neutral"Show full SKILL.md (288 more words)Show less
6. Seasonality
Oil seasonality:
- March-May: refinery maintenance ends + summer inventory build → seasonal rise (ahead of the "driving season")
- September-October: hurricane season (Gulf of Mexico) → supply disruption → higher volatility
- November-December: heating-oil demand → stronger diesel crack spread
Gold seasonality:
- January-February: Lunar New Year + Indian wedding-season physical demand → relatively strong
- July-August: traditional soft season → relatively weak
- October-November: Diwali + Christmas restocking → relatively strong
Copper seasonality:
- March-April: China construction season starts → demand recovery
- June-July: off-season inventory buildup → pressure
- September-October: "Golden September, Silver October" → demand recovery
Analysis Framework
Five-Step Commodity Analysis
- Supply-demand sets direction: is the balance in surplus or shortage? Which way are marginal variables moving?
- Inventory sets rhythm: which inventory-cycle stage are we in? Is a turning point close?
- Term structure confirms: contango or backwardation? Does it confirm the supply-demand judgment?
- Seasonality overlay: is seasonality currently a tailwind or a headwind?
- Macro validation: do the dollar / rates / risk appetite support the directional judgment?
Composite Scoring Template
commodity_score = {
"supply_demand": +1, # supply-demand is tight
"inventory_cycle": +2, # passive destocking (best stage)
"term_structure": +1, # mild backwardation
"seasonality": 0, # neutral seasonality
"macro_env": -1, # stronger dollar is a headwind
}
# Total score = +3/5 = +0.6 -> bullish bias, but not a strong signalOutput Format
## Commodity Analysis Report — [Commodity Name]
### Supply-Demand Structure
- Supply side: [surplus / balanced / shortage] — [specific data]
- Demand side: [strong / stable / weak] — [specific data]
- Balance table: [inventory build X tons / drawdown X tons]
### Inventory Cycle
- Current stage: [active restocking / passive restocking / active destocking / passive destocking]
- Visible inventory: [LME X tons, SHFE X tons, WoW change]
### Term Structure
- Front-back spread: [contango X% / backwardation X%]
- Roll yield: [positive / negative]
### Composite Score
| Dimension | Score(-2~+2) | Basis |
|------|------------|------|
| Supply-demand | +1 | OPEC compliance rate 92% |
| Inventory | +2 | LME inventory hit 18-month low |
### Trading Direction
- Direction: [bullish / bearish / neutral]
- Confidence: [high / medium / low]
- Risk points: [specific risks]Notes
- Commodity data sources are fragmented (EIA / OPEC / LME / SHFE, etc.). This skill provides the analytical framework; data should be retrieved through
web-readeror entered manually - Futures prices include roll costs, so direct comparison across different contracts must account for expiry-roll effects
- Seasonal patterns are statistical averages and may be completely overwhelmed by fundamentals in a given year
- Gold has both commodity and financial attributes, and the financial side (rates / dollar) usually dominates short-term pricing
- Copper’s financial characteristics have strengthened since 2020 (copper futures are used as a macro hedge), so pure fundamental analysis may be insufficient
- Inventory data is lagged (hidden inventories cannot be tracked in real time), so cross-check with price and basis behavior
- This framework is for research backtesting only and does not constitute investment advice
© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in agent/src/skills/commodity-analysis of HKUDS/Vibe-Trading.
Open the folder on GitHubat commit 7f6908b
Compare with similar skills
Commodity Analysis Signals next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Commodity Analysis Signals this skillHKUDS/Vibe-Trading | 35k | — | ~2.3k | Automated safety check: Pass | MIT | |
| Strategy Performance Reporttradesdontlie/tradingview-mcp | 6.8k | 3 repos | ~591 | Automated safety check: Pass | Custom licence | |
| Regimejackson-video-resources/markov-hedge-fund-method | 483 | — | ~1.6k | Automated safety check: Pass | Custom licence | |
| Quant Buddy Market Data and Backtestingpseudo-longinus/quant-buddy-skills | 191 | 1 repos | ~11k | Automated safety check: Pass | MIT | |
| Alpha Desk Investment ResearchJingHao-Leon/dsh-alpha-desk | 181 | — | ~1.3k | Automated safety check: Notes | MIT | |
| Revops Revenue Planningswan-gtm/gtm-skills | 165 | — | ~7.2k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Commodity Analysis Signals
What does Commodity Analysis Signals do?
Analyzes crude oil, gold and copper through supply-demand balance, pricing models, inventory cycles and futures structure, and outputs directional signals suitable for backtesting. The skill examines commodities along four lines: supply-demand balance, a pricing model, the inventory cycle and futures curve structure, and it turns the result into directional signals that can be backtested. It concentrates on crude oil as the global pricing anchor, gold as a safe haven and inflation hedge, and copper as an economic barometer.
When should I use Commodity Analysis Signals?
Commodity Analysis Signals fits situations like: judging whether the oil market is tight or loose from supply and demand data; assessing gold with real rates, the dollar index and safe-haven demand; using copper as a read on industrial activity; generating directional commodity signals for a backtest.
How do I install Commodity Analysis Signals in Claude Code?
Run `npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a claude-code`. Or copy the skill folder (agent/src/skills/commodity-analysis in HKUDS/Vibe-Trading) into .claude/skills/commodity-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Commodity Analysis Signals in Codex?
Run `npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a codex`. Or copy the skill folder (agent/src/skills/commodity-analysis in HKUDS/Vibe-Trading) into .agents/skills/commodity-analysis in your project. Codex loads it when a task matches its description.
Can I use Commodity Analysis Signals in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/Vibe-Trading --skill commodity-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/commodity-analysis, .gemini/skills/commodity-analysis, .github/skills/commodity-analysis and .opencode/skills/commodity-analysis in your project.
What does Commodity Analysis Signals need to run?
SKILL.md names no scripts, command-line tools or credentials: Commodity Analysis Signals is instructions for the agent only.
Does Commodity Analysis Signals access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Commodity Analysis Signals safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Commodity Analysis Signals use?
Commodity Analysis Signals is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Commodity Analysis Signals use?
About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Commodity Analysis Signals?
Skills that share tags, products or a category with Commodity Analysis Signals: Strategy Performance Report (tradesdontlie/tradingview-mcp, 6.8k stars), Regime (jackson-video-resources/markov-hedge-fund-method, 483 stars), Quant Buddy Market Data and Backtesting (pseudo-longinus/quant-buddy-skills, 191 stars) and Alpha Desk Investment Research (JingHao-Leon/dsh-alpha-desk, 181 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Commodity Analysis Signals?
HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 34,884 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 6, 2026.
Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.