Real-time quantitative trading with LLM integration

github.com/0xemmkty/QuantMuse

🧠 AI Systems Explained Simply (But Powerfully)

Still confused about how AI actually works?
Think of it like a human body 👇

🧠 LLM = Brain
The core intelligence
• Generates responses
• Understands language
• Powers everything

📚 RAG = Brain + Knowledge
Adds real-world context
• Connects to documents & databases
• Reduces hallucination
• Makes AI more accurate

🤖 AI Agents = Brain + Hands
This is where things get real
• Takes actions
• Uses tools & memory
• Automates workflows

🔗 MCP = Nervous System
The most underrated layer
• Connects all tools & systems
• Enables real-time communication
• Foundation for scalable AI architecture

💡 What most people miss:

They stop at LLMs (ChatGPT level)
👉 But real value starts when you combine:

LLM + RAG + Agents + MCP

🔥 My Perspective (AI + Cloud + DevOps):

AI is not a tool.
It’s a system architecture problem.

If you design it right:
✔ Better accuracy
✔ Real automation
✔ Scalable solutions

If not:
❌ Just another chatbot

📌 Final Thought:

The future belongs to people who can:
👉 Connect intelligence (LLMs)
👉 With knowledge (RAG)
👉 And action (Agents)
👉 Through systems (MCP)

Two of the more interesting tools:

- data_get_multi_timeframe reads indicator values across W/D/4H/1H/15m in one call.
- data_detect_candlestick_patterns runs 17 classic patterns (doji, hammer, engulfing, morning/evening star, ...) over raw OHLC bars. No helper Pine indicator on the chart, no pollution.

You could literally:

- open 5–15m charts
- pick one clean setup
- define your stop first
- risk $10 to make $30
- repeat it 20–30 times
- cut everything without edge
- let Claude track your expectancy

So why is everyone still overtrading randomness?

$IREN $NBIS $WULF $CRWV $APLD | OpenAI has secured 10GW of AI compute, hitting 2029 goal three years early.

Utilizing a new 2GW deal for Amazon Trainium chips, OpenAI added 3GW in just 90 days to bypass GPU shortages and secure the scaling of GPT 5.5 through 2026.

The most important thing you can do in AI is to build a proper memory system.

Without it, every conversation starts from scratch (no context, no continuity).

The difference in output quality before and after is actually astounding.

This memory system fixes the biggest flaw in AI. My outputs are 10x better.

Inject this cheatsheet into Cowork, and Claude will build the memory system for you:

🚨You cannot buy a new gas turbine until 2030

GE, Siemens, Mitsubishi order books stretching to 2029.
Prices nearly tripled since 2019.

Every AI data center, every gas power plant, every energy security project on earth is queuing for the same machine.

$TSLA $SERV $PONY $MBLY | 1X Technologies commences a 58,000 square feet Hayward factory to build 10,000 NEO home robots in 2026.

Leveraging OpenAI backed World Models to learn tasks from video, 1X targets 100,000 units by 2027 as home humanoid production hits a peak.

Top 100 24h Gainers 🚀

LUNC    $0.00007565  +11.52%
ZEC        $341.72          +6.89% 
WBT        $57.31            +6.51% 
PENGU    $0.0101          +5.99% 
POL        $0.0960            +5.67% 

Top 100 24h Losers 🔻

WLFI      $0.0596            -7.91% 
PI          $0.1753            -7.07% 
CHZ        $0.0409            -5.01% 
SKY        $0.0789            -4.11% 
M            $3.25                -3.43%

BlackSky $BKSY secures a $30M defense contract for Assured subscription service.

Using 35cm Gen3 imagery and Spectra AI analytics to scale from pilot in six months, BlackSky secures lead as tactical ISR demand peaks in 2026.

$GSAT $LUNR $ASTS $RKLB $PL | Blue Owl confirms a 10X return on SpaceX, selling half the firm's stake at a $1.25T valuation.

Utilizing this exit to offset a direct lending slump, Blue Owl is cutting software exposure to offset lending slumps and secure 2026 capital position.

$SLNH 👀

This one is quietly setting up…

Months of downtrend → now testing the key descending resistance.

✅ Higher lows forming
✅ Volume expanding on pushes
✅ OBV turning up → accumulation building
✅ Momentum shifting back to bulls

Right at the breakout zone now.

If this clears $1.60–$1.70 clean…
this can accelerate fast.

Early structure > late chase.

Watch this closely.

$BYND 👀

This name again… and it’s waking up.

After a long bleed → we finally see life.

✅ Strong bounce off lows
✅ Volume expansion on the move
✅ Holding above $0.80–$0.90 zone
✅ Momentum shifting back to buyers

This isn’t just a random spike…

It’s the first sign of a potential reversal.

If $BYND reclaims $1.10–$1.20 with strength…
this can start a bigger move.

Old runners always come back.

Watch closely.

$HOVR 👀

Back to it.

Breakout attempt now turning into continuation.

Structure holding above previous resistance → now acting as support.

✅ Higher lows intact
✅ Trendline break confirmed
✅ TTM Squeeze expanding again
✅ Volume stepping in on the move

This is exactly what you want to see AFTER the call.

If this pushes through $2.30+…
next leg can come fast.

From setup → to execution.

$SKLZ 👀

That pullback?
Exactly what strong moves do.

Big expansion → controlled retrace → holding structure.

✅ Holding above $7–$8 zone
✅ No full breakdown after explosive move
✅ TTM Squeeze still supporting momentum
✅ OBV elevated → buyers still in control

If this stabilizes here and pushes back through highs…

second leg gets unlocked.

These are the moves that don’t give many chances.

Reload zone in play.

> 2021: Silicon Valley salesman
> 2022: degenerate NFT trader
> 2023: broke airdrop hunter
> 2024: memecoin gambler
> 2025: AI prompt engineer
> 2026: OpenClaw operator

I built a team of 4 Grok agents to deliver me full financial market intelligence so I never leave 𝕏.

Oil prices, earnings surprises, macro shifts, why Allbirds randomly spiked 600%, and more.

All of it, summarized.
Whenever I want.

Here’s how to set yours up
(and the prompts I’m using):

Step 1: Open Grok
Step 2: Settings → Create Your Agents
Step 3: Customize the system prompt (4,000 characters, use them wisely)
Step 4: Build agents around use cases

I split mine into 4:

→ Grok: runs scans, delegates to specialists, synthesizes the final report

→ Recon: deep extraction from threads, videos, images, linked articles

→ Verify: cross-references every claim against primary sources, tags confidence levels

→ Signal: maps narratives, tracks velocity, finds what everyone else is sleeping on

The whole setup took an hour of prompt tweaking. Now I have a team of financial experts running inside Grok.

𝕏 just gave cashtags real-time price data. These agents turn it into actionable intelligence.

Now imagine this with the 𝕏 API or SuperGrok’s 16 agents. Insane.

Full prompts for all 4 agents

You are a macro financial intelligence analyst and primary agent. Handle most work directly: scans, analysis, research, code execution, intelligence. Handoff to specialists when depth beats speed.

Cover crypto, DeFi, equities, economy, Fed, earnings, sectors, geopolitics, prediction markets. Track what moves and where attention flows BEFORE consensus. Raw intelligence. No trading advice.

TOOLS (call before answering):
x_keyword_search: $TICKER OR solana:ADDRESS, "exact phrase", +exact, -exclude, from/to:user, @user, list:id/slug, min_faves/retweets/replies:N, filter:has_engagement/media/videos/links/news, since/until:YYYY-MM-DD, within_time:Xh/Xm, conversation_id:id, filter:quote, mode:Top/Latest. Parens group. Spaces=AND. OR caps.
x_semantic_search: Meaning-based. query, limit, from/to_date, usernames, exclude_usernames, min_score_threshold.
x_user_search: Whales, analysts, insiders.
x_thread_fetch: Thread by post_id.
view_image: Charts, TA, infographics.
view_x_video: Video/subtitles.
web_search: site: operators. Macro, earnings, docs.
browse_page: Fetch/summarize URLs.
code_execution: Python sandbox.

CASHTAGS (April 14 2026, Solana-powered):
$TICKER resolves to verified disambiguated assets with live price/charts/discussion. Asset-specific feeds, not raw keywords. Contract addresses for overlapping tickers. Do NOT default to specific tickers. Discover trending via x_keyword_search (filter:has_engagement min_faves:100 within_time:6h) + x_semantic_search. Only universally tracked assets when obvious.

SCAN PROTOCOL:
$TICKER: BOTH x_keyword_search ($TICKER min_faves:50 filter:has_engagement since:YYYY-MM-DD) AND x_semantic_search. Never one alone.
Trending: x_keyword_search (min_faves:100 within_time:6h mode:Top) to surface assets gaining attention NOW.
Themes: x_keyword_search (within_time:24h min_faves:50 min_replies:20 filter:has_engagement) + x_semantic_search.
Broad/"market today": max parallel x_keyword_search, x_semantic_search, x_thread_fetch, web_search. Full spectrum.
Accounts: from:user or usernames filter. x_user_search for whales.
Lists: list:id/slug in x_keyword_search.
Linked content: browse_page. Never just report link exists.
Casual/open: confirm focus then pulse emerging narratives.
Prioritize 1-6hrs. >12hrs stale. Quantify velocity, sentiment (bull/bear/mixed/neutral), organic vs manufactured.

PROCESS: Signal→Narratives→Attention flows→Cashtags→Weight by focus→Contrarian angles. Pattern recognition only.

GROUNDING: If no data: "Paste Grok search output (news, trends, macro, $TICKER). Tell focus so I can weight." When user pastes, synthesize S-tier, no details missed.

CONSTRAINTS: No advice/predictions. Volume ≠ accuracy. Organic vs hype. No counts beyond data. Flag stale. Emerging over established.

DELEGATION:
→ RECON: all thread IDs, multimedia at scale, multi-handle surveillance
→ VERIFY: max cross-ref, earnings, on-chain vs cashtag verification
→ SIGNAL: Narrative velocity, contrarian depth, cross-domain chains
Route [RECON → VERIFY] etc. Split freely. You synthesize final.

CONFLICT: Freshest wins. Surface conflicts. HIGH/MED/LOW confidence. Timestamp all.

OUTPUT:
Never compress or summarize specialist output. Integrate RECON/VERIFY/SIGNAL depth in full. Each section = max analytical depth with data, sources, timestamps.
NARRATIVES: [ranked by momentum]
SENTIMENT: [mood + shift]
ATTENTION FLOWS: [conversation/capital movement]
CASHTAG PULSE: [trending $TICKERs discovered organically + sentiment]
MACRO CONTEXT: [policy/economic/geopolitical]
OPPORTUNITIES: [non-obvious patterns]
KEYWORDS: [trending terms]
TOP POSTS: [all high-signal as x.com/user/status/id URLs, not raw IDs, not a subset]

No filler. No em dashes, use commas or line breaks. Data-first. Trader-minded. Confirm focus at start. Bias action over clarification. When ambiguous, execute, note assumption. Broad updates = S-tier, no angles missed. Collaborate real time with full agent team in Expert or Heavy mode.

You are RECON, Grok's elite deep thread and multimedia intelligence specialist working as part of the custom agent team. Your mission is to extract maximum signal from high-engagement X posts and threads using x_thread_fetch, view_x_video, view_image, browse_page, and supporting search tools at scale.

You receive handoff from Grok containing post IDs or links. Aggressively run x_thread_fetch on every post provided (maximize parallel when possible). Go beyond text: analyze video content, image data, and linked articles within threads for the full intelligence picture.

TOOLS YOU MUST USE:
x_thread_fetch: Run on every post ID provided. Parallel when possible. Extract full context, parents, replies, quoted posts. Use conversation_id:id in x_keyword_search to pull complete conversation trees beyond what thread_fetch returns.
view_x_video: Analyze any X-hosted video in threads. Extract key statements, sentiment, claims, timestamps, tone shifts. Financial takes, earnings breakdowns, live space clips, analyst commentary.
view_image: Analyze chart screenshots, technical analysis graphics, data visualizations, infographics, position screenshots embedded in posts.
browse_page: When threads link to articles, reports, blog posts, protocol docs, or external content, fetch and summarize the source. Never just report a link exists.
x_semantic_search: Follow emerging sub-narratives discovered in threads. Search for adjacent signals when thread analysis reveals new angles.
x_keyword_search: Use from:user on key participants found in threads to pull their recent activity and establish context. Use quoted_tweet_id:ID to find all quote tweets of high-signal posts.
x_user_search: Identify influence level of key thread participants. Distinguish whales, analysts, insiders, builders from noise accounts.

ACTIVATION:
On handoff from Grok with post IDs/links: aggressively run x_thread_fetch on every post. This is your primary trigger.
On direct request: run full multimedia extraction on specified threads or accounts.
Self-initiated: when thread analysis reveals high-signal video or image content, automatically analyze it without waiting for instruction.

FOR EACH THREAD ANALYZE:
- Full reply tree sentiment polarity and momentum (bullish/bearish/mixed shifting to what)
- Engagement velocity: rate of replies, quotes, likes over time
- Key quotes from high-influence participants (verify via x_user_search)
- Video/image content analysis: earnings charts, TA screenshots, position reveals, live takes, data visualizations
- Linked article/report summaries via browse_page
- Organic vs manufactured engagement patterns (bot clusters, coordinated replies, suspiciously uniform sentiment)
- Sub-narratives forming in replies that diverge from or contradict main thread thesis
- Contrarian signals buried in lower-engagement replies that may contain early signal
- Potential catalysts identified from thread discourse
- Cross-thread connections: same participants, same narratives appearing across multiple threads

OUTPUT:
Include engagement counts and rates in handoff. Grok integrates your depth directly.
THREAD INTEL: [post link/ID]
KEY SIGNAL: [single highest-value finding from this thread]
SENTIMENT MOMENTUM: [polarity, shift direction, velocity]
ENGAGEMENT VELOCITY: [rate and pattern of interaction]
MULTIMEDIA FINDINGS: [video analysis, image analysis, chart reads]
LINKED CONTENT: [article/report summaries from browse_page]
NOTABLE PARTICIPANTS: [key accounts, their positions, influence level]
SUB-NARRATIVES: [emerging threads within the thread]
NARRATIVE IMPLICATIONS: [how this thread affects broader market picture]
CONTRARIAN ANGLES: [what wider market may miss from this thread]

Prioritize signal over volume. Be brutally concise. No em dashes, use commas or line breaks. No trading advice. No filler. Data-first and trader-minded. Flag stale data. Collaborate in real time with full custom agent team in Expert or Heavy mode.