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Day Trading with Smart Money Concepts — Intraday Structure, Liquidity Hunts, and Kill Zones

Day Trading with Smart Money Concepts — Intraday Structure, Liquidity Hunts, and Kill Zones

By HorizonAI Team

Smart Money Concepts (SMC) are especially popular with day traders—scalpers hunting intraday liquidity, traders watching kill zones, and people looking for precise entries on 1m–15m charts.
Used well, SMC can give structure to fast markets. Used poorly, it becomes noise and over‑trading.

This guide shows you how to apply SMC to day trading in a structured way:

  • How to read intraday market structure across 1m–15m
  • How to use Asian, London, and New York sessions (kill zones) as liquidity engines
  • How to build day‑trading narratives: liquidity sweeps → CHOCH → OB/FVG entries
  • How to start turning these ideas into Pine Script v6 playbooks on EURUSD
  • How HorizonAI can help you automate and test SMC day‑trading systems

Step 1: Multi-Timeframe Structure for Day Trading

Even when trading 1m–5m, you don’t start there.

Higher-Timeframe Bias

Use 4H and 1H for bias:

  • Bullish:
    • HH/HL structure
    • Recent bullish BOS
    • Price in discount on that higher‑timeframe swing
  • Bearish:
    • LH/LL structure
    • Recent bearish BOS
    • Price in premium on the swing

You’re not trying to scalp against the 4H trend all day—you’re trying to:

  • Align intraday trades with larger flows
  • Fade intraday liquidity hunts back into the higher‑timeframe direction

Execution Timeframes

For the intraday SMC work:

  • 15m: intraday structure (mini trends, intraday BOS/CHOCH)
  • 5m: entries and confirmation
  • 1m: optional fine‑tuning (only if you can handle the noise)

Step 2: Understand Kill Zones and Session Liquidity

Different sessions have different personalities:

  • Asia: often slower, range‑bound on many pairs
  • London: volatility expansion, breakouts, stop hunts
  • New York: continuation or sharp reversals, extra liquidity

In many ICT‑style models, “kill zones” are:

  • Specific time windows (e.g., London open, New York open)
  • Where liquidity is built and raided

Practical Windows (UTC Approximation)

  • London session: 07:00–11:00
  • New York session: 13:00–17:00

You’ll often see:

  • Asia builds a range
  • London or New York sweeps one end of that range
  • Price then moves in the opposite direction for the session

Step 3: Intraday Liquidity Hunts

Day‑trading SMC revolves around intraday liquidity:

  • Asian highs/lows
  • London highs/lows
  • Previous day high/low
  • Equal highs/equal lows on 5m/15m

Common narrative:

  1. Session opens near well‑defined highs/lows
  2. Price spikes through (stop hunt)
  3. Strong rejection + CHOCH on lower timeframe
  4. Price trades back into OB/FVG in discount/premium

You then trade away from the liquidity grab in line with higher‑timeframe context.

Step 4: Example Day-Trading Playbook (EURUSD)

Let’s outline a complete bullish New York model for EURUSD.

Higher-Timeframe Context

  • 4H trend bullish (HH/HL, bullish BOS)
  • Price in discount on 4H

London Session

  • London builds a range and sets a London low
  • Price holds above previous day low

New York Open (Kill Zone)

  1. New York dips below the London low or previous day low
  2. Creates a liquidity grab (long wick, closes back inside range)
  3. On 5m:
    • Price forms bearish CHOCH into the low
    • Then a bullish CHOCH or BOS upward
  4. A bullish OB and/or FVG forms as price pushes away from the sweep
  5. Price retraces into that OB/FVG in discount

Entry and Management

  • Entry: long from 5m OB/FVG zone after the sweep
  • Stop: below the liquidity low (sweep low)
  • Targets:
    • First: prior intraday high or 2R
    • Second: opposing session liquidity (e.g., London high)

You can trade this playbook manually—or codify it step‑by‑step with HorizonAI.

Step 5: Session and Liquidity Markup in Pine (Skeleton)

Here’s a simple session + previous day high/low harness for EURUSD.

//@version=5
indicator("Day Trading SMC: Sessions & Liquidity", overlay = true)

// Previous day high/low
var float prevHigh = na
var float prevLow  = na

isNewDay = ta.change(time("D"))

if isNewDay
    prevHigh := high[1]
    prevLow  := low[1]
else
    prevHigh := math.max(prevHigh, high)
    prevLow  := math.min(prevLow, low)

plot(prevHigh, "Prev Day High", color = color.red)
plot(prevLow,  "Prev Day Low",  color = color.green)

// Sessions (UTC approx.)
hour = tonumber(str.format("{0,time,HH}", time))

inLondon = hour >= 7 and hour < 11
inNewYork = hour >= 13 and hour < 17

bgcolor(inLondon ? color.new(color.blue, 94) : inNewYork ? color.new(color.purple, 94) : na,
     title = "London / New York")

On top of this, HorizonAI can layer:

  • Asian range detection
  • Liquidity sweep detection (wicks beyond these levels)
  • BOS/CHOCH logic on 5m
  • OB/FVG entries and risk management

Step 6: Risk and Trade Frequency for SMC Day Trading

SMC day trading is intense:

  • Many signals
  • High emotional load
  • Easy to over‑trade

Rules that keep you alive:

  • Risk 0.5–1% per trade (day trading doesn’t need 3–5% risk)
  • Hard cap on trades per session (e.g., max 3 trades London, 3 New York)
  • Hard daily loss limit (e.g., stop after –3% or 3 losers)
  • No revenge trading after a loss—your playbook doesn’t change mid‑session

These can all be encoded directly into Pine Script so your backtests respect realistic behavior.

Step 7: Avoid Your Biggest SMC Day-Trading Pitfalls

❌ Over-Labeling Every Move

On 1m charts, you can find a BOS and CHOCH every few bars.
Don’t label everything—focus on clear, meaningful swings.

❌ Ignoring Higher-Timeframe Context

Scalping every liquidity grab against the 4H trend is a fast way to bleed out.

Tie each intraday idea back to:

  • 4H/1H structure
  • Daily and previous day levels

❌ No Data on Your Playbook

If you can’t answer:

  • “What’s the win rate of my New York sweep model?”
  • “What’s the average RR and drawdown over 100+ trades?”

…you’re guessing, not trading a system.

That’s exactly what backtesting + HorizonAI can fix.

Use HorizonAI to Turn Your Day-Trading SMC Playbook into Code

Instead of trying to write all the logic from scratch, you can:

  • Describe your exact day‑trading rules in plain English:
    • Session windows
    • Liquidity sweeps
    • Structure requirements
    • OB/FVG logic
    • Risk parameters
  • Let HorizonAI generate Pine Script v6 strategies and indicators that:
    • Mark your kill zones and liquidity levels
    • Detect your specific sweep patterns
    • Filter trades by higher‑timeframe bias
    • Apply risk rules and record performance

Example prompts:

"Create a Pine Script v6 strategy for EURUSD 5m that trades only during London and New York, long after New York sweeps the London low in a 4H uptrend, entering from a 5m bullish OB in discount with 1% risk and 2.5:1 RR."

"Build an indicator that marks Asian range highs/lows, highlights London and New York sessions, and tags candles that sweep those levels and close back inside the range."

"Generate a day-trading dashboard that shows my daily P&L, number of trades, and whether I’ve hit my max trade or loss limit."

Automate your SMC day-trading playbook with HorizonAI →

Final Thoughts

Day trading with SMC doesn’t have to be chaotic if you:

  1. Anchor on higher‑timeframe structure (4H/1H)
  2. Use sessions and kill zones to frame when liquidity is likely to be hunted
  3. Look for clean narratives: liquidity sweeps → CHOCH/BOS → OB/FVG in premium/discount
  4. Keep risk small and trade counts capped
  5. Turn your rules into code and data instead of relying purely on feel

Once you’ve defined your intraday SMC playbook, HorizonAI can help you turn it into Pine Script, backtest it on EURUSD (or any market you like), and evolve it into a robust, testable day‑trading system.

Want feedback on a specific kill‑zone model or SMC day‑trading idea? Join our Discord community and trade notes with other intraday SMC traders.