p0z402
p0z402
[DOCUMENTATION]

p0z402 Docs

01

Overview

p0z402 analyzes 8 independent on-chain liquidity signals for Solana tokens and synthesizes them into a single Flux Index (0-100) with a FluxState classification. Instead of reacting to price, it detects the liquidity conditions that *cause* price movement. LP Depth ──┐ Whale Flow ─┤ Buy Pressure─┤ Sell Wall ──┤──> Flux Index > FluxState > Action Vol Velocity┤ Price Impact┤ Holder Flux ┤ Arbitrage ──┘

02

Eight Signals

1. buy_pressure (20%) — Volume dominance + transaction count ratio 2. whale_flow (15%) — Average trade size vs whale threshold 3. volume_velocity (15%) — Unique traders + volume-to-LP ratio 4. lp_depth (15%) — LP growth/decline and absolute depth 5. sell_wall (15%) — Sell pressure × price impact intensity 6. holder_flux (10%) — Holder count change velocity 7. price_impact (5%) — LP depth quality via slippage 8. arbitrage (5%) — Cross-DEX efficiency and manipulation

03

FluxState

STABLE (0-19) — No significant flux STIRRING (20-39) — Early movement, unconfirmed FLOWING (40-59) — Directional momentum building RUSHING (60-79) — Strong flux, imminent action SURGING (80-100) — All signals aligned — breakout

04

Installation

[terminal]
pip install p0z402

# Or from source
git clone https://github.com/p0z402.git
cd p0z402
pip install -e ".[dev]"
05

Usage

[terminal]
from p0z402 import P0zEngine, LiquiditySnapshot
import time

engine = P0zEngine()

snap = LiquiditySnapshot(
    token_ca="So11111111111111111111111111111111111111112",
    token_name="MyToken",
    timestamp=time.time(),
    lp_depth_usd=250_000,
    lp_depth_change_1h=18.5,
    lp_depth_change_24h=35.0,
    buy_volume_1h=90_000,
    sell_volume_1h=25_000,
    buy_count_1h=180,
    sell_count_1h=40,
    large_buy_threshold=5_000,
    price_usd=0.0042,
    price_change_1h=7.3,
    price_impact_1k=0.28,
    holder_count=1200,
    holder_change_1h=85,
    unique_traders_1h=120,
    dex_count=4,
    arbitrage_gap=0.35,
)

result = engine.analyze(snap)

print(f"Flux Index: {result.flux_index}/100")
print(f"State: {result.flux_state.value.upper()}")
print(f"Breakout: {result.breakout_probability:.0f}%")
print(f"Action: {result.recommended_action}")
06

Dashboard

Radar chart of all 8 signal scores + flux state timeline.

[terminal]
cd dashboard
npm install
npm run dev
# → http://localhost:3000
07

Docker

[terminal]
docker-compose up p0z402   # run tests
docker-compose up           # tests + dashboard
08

Tests

[terminal]
pytest tests/ -v
# 116 tests across signals, engine, agents, and utilities