liquidity-zones

name	liquidity-zones
description	Mark candidate liquidity levels and test sweep/reversal behavior. Use when analyzing equal highs/lows, swing levels, round-number clustering, or stop-loss cascade risk without inferring hidden intent.
license	Apache-2.0
metadata	{"author":"ske-labs","version":"4.0"}

Liquidity Zones Trading

Observable price levels where stop or limit orders may cluster. Clustering can contribute to cascades, but candles alone cannot identify the orders, their owners, institutional targeting, or a guaranteed reversal.

Identification

Buy-Side Liquidity (BSL)

• Location: Above swing highs, equal highs, resistance
• Hypothesis: Some buy stops may rest above the level; verify with available order/trade data

Sell-Side Liquidity (SSL)

• Location: Below swing lows, equal lows, support
• Hypothesis: Some sell stops may rest below the level; verify with available order/trade data

Level Features

Record formation type, number of touches, equality tolerance in ATR/ticks, age, visible depth if available, distance, and time of day. Calibrate these features rather than assigning ordinal strength.

Workflow

1. Identify liquidity pool (equal highs/lows, obvious swing points):

   get_candles_around_date(symbol=<symbol>, exchange=<exchange>, interval=<interval>, date=<date>)
   

2. Mark levels using draw_chart_analysis: resistance for BSL, support for SSL, highlight for sweep points
3. Wait for sweep — price takes out the level
4. Confirm reversal: use an objective swing/rejection rule and a predeclared return window
5. Enter after confirmation
6. Stop loss beyond the sweep wick
7. Target opposite liquidity pool

Evidence and Validation

• Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
• Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
• Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
• Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
• Research basis: Osler found clustered FX stop-loss orders can contribute to price cascades. This supports conditional liquidity hypotheses, not certainty that institutions target a visible level.

Key Rules

• Define sweep distance, close condition, reversal window, and expiry before evaluation.
• Compare anticipation, reversal, and continuation hypotheses on identical samples.
• Use time-of-day relative volume; it is contextual, not proof of stop orders.
• Equal highs/lows are candidate clustering coordinates, not known liquidity or magnets.
• Return no trade when the level or trigger cannot be defined without hindsight.

Related Skills

• kill-zones — Liquidity sweeps most commonly occur during London and NY AM sessions
• market-structure-shift — CHoCH after a sweep confirms the reversal direction