mcpField
E
emerging-movers

emerging-movers

github.com/senpi-aiData & databases140197 installsUpdated today
Repository

About emerging-movers

emerging-movers is a skill that tracks Smart Money profit concentration across Hyperliquid assets and flags markets gaining rank. It compares repeated leaderboard scans and applies quality filters to distinguish stronger moves from noisy signals.

Key features

  1. 01Track Smart Money leaderboard changes
  2. 02Detect rank and contribution acceleration
  3. 03Filter erratic or low-velocity signals
  4. 04Apply trader-count and leverage thresholds
  5. 05Output structured alerts and mover lists

Use cases

  1. 01Monitor assets rising through the leaderboard
  2. 02Flag new entries into the top 20
  3. 03Filter potential movers before scanner review
  4. 04Trigger follow-up analysis of flagged assets

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/senpi-ai/senpi-skills --skill emerging-movers

After installing, type/emerging-moversin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
examples.md
output-schema.md
quality-filters.md
scripts/
emerging-movers.py

Details

From the project README

Emerging Movers Detector v3.1

Tracks Smart Money market concentration across all Hyperliquid assets and flags assets accelerating up the ranks before they become crowded top-3 plays. By the time an asset hits the top of the SM leaderboard, the easy money is gone. This catches the trajectory.

One API call per scan. Near-zero LLM tokens. Runs every 60 seconds.

How It Works

The SM Profit Concentration Leaderboard

Senpi's leaderboard_get_markets returns all assets ranked by percentage of total Smart Money profit in the last 4-hour rolling window. This isn't trader count — it's where the money is actually flowing.

#1  ETH SHORT   31.4%  286 traders
#2  BTC SHORT   25.1%  436 traders
#3  HYPE SHORT  24.2%  330 traders
...
#36 ASTER SHORT  0.2%   18 traders  ← 60s later: #13, 0.82%, 65 traders

The script tracks this leaderboard over time and detects acceleration.

Detection Signals

Immediate Action Signals (v3+)
SignalConditionPriority
IMMEDIATE_MOVER10+ rank jump from #25+ in ONE scanHighest — act now
NEW_ENTRY_DEEPAppears in top 20 from nowhereVery high
CONTRIB_EXPLOSION3x+ contribution increase in one scanVery high
DEEP_CLIMBER5+ rank jump from #25+High
Trend Signals
SignalCondition
NEW_ENTRYFirst appearance in top 50
RANK_UPJumped 2+ positions in one scan
CLIMBING3+ positions up over several scans
ACCELContribution % increasing scan-over-scan
STREAKConsistently climbing every check
VELOCITYSustained positive contribution growth
v3.1 Quality Filters

These prevent false IMMEDIATE signals that looked great on rank jump alone but failed on execution:

FilterRuleRationale
Erratic rank>5 rank reversals in history → erratic: true, downgradedBouncing ranks are noise
Velocity gatecontribVelocity < 0.03 → lowVelocity: true, excluded from IMMEDIATENo momentum behind the move
Trader count floor<10 traders → SKIP IMMEDIATESingle whale risk
Max leverage checkmax leverage < 10x → SKIPNot worth the limited position sizing

See references/quality-filters.md for implementation details and real-world examples.

Architecture

┌────────────────────────────────────┐
│ Cron: every 60 seconds             │
├────────────────────────────────────┤
│ scripts/emerging-movers.py         │
│ • Loads scan history from JSON     │
│ • Fetches leaderboard (1 API call) │
│ • Parses top 50 markets            │
│ • Compares with previous scans     │
│ • Detects signals + v3.1 filters   │
│ • Saves updated history            │
│ • Outputs JSON with alerts         │
├────────────────────────────────────┤
│ Agent reads output:                │
│ • IMMEDIATE alerts → evaluate now  │
│ • Deep climbers → queue for review │
│ • No alerts → silent               │
└────────────────────────────────────┘

Files

FilePurpose
scripts/emerging-movers.pyScanner script
emerging-movers-history.jsonAuto-managed scan history (last 60 scans)
max-leverage.jsonOptional: asset max leverage reference

Output

See references/output-schema.md for the complete JSON schema.

Key top-level fields: alerts[], topMovers[], immediateMovers[], deepClimbers[], scanCount, timestamp.

Per-alert fields: asset, direction, rank, prevRank, contribution, traderCount, reasons[], contribVelocity, isImmediate, isDeepClimber, erratic, lowVelocity.

Cron Setup

*/1 * * * * python3 scripts/emerging-movers.py
Agent Response Logic
  • isImmediate: true + erratic: false + lowVelocity: false → Evaluate immediately for entry via Scanner
  • isDeepClimber: true → Queue for next scanner run
  • erratic: true or lowVelocity: true → Log but do not act
  • No alerts → Silent

Companion skills

  • opportunity-scanner — use Scanner to deep-dive assets flagged by Emerging Movers
  • autonomous-trading — full loop integrating Emerging Movers as entry trigger
  • wolf-strategy — uses IMMEDIATE_MOVER as primary entry signal

Comments

Markdown supported