Smc Python Library

smc-python-library

USE FOR:

• "SMC indicators in Python"
• "FVG / order block / BOS / CHoCH detection in code"
• "ICT kill zones in Python"
• "smart money concepts library"
• "programmatic SMC analysis on OHLC data"
• "liquidity sweeps detection Python" tags: [SMC, ICT, Python, FVG, order-blocks, BOS, CHoCH, liquidity, swing-highs-lows, kill-zones] kind: library category: ict-smart-money

What Is smartmoneyconcepts?

Python library implementing all core ICT/SMC indicators on OHLC DataFrames.

• Repo: https://github.com/joshyattridge/smart-money-concepts
• Install: pip install smartmoneyconcepts
• Input: pandas DataFrame with columns ["open", "high", "low", "close", "volume"]

Installation

pip install smartmoneyconcepts

from smartmoneyconcepts import smc
import pandas as pd

Full API Reference

1. Fair Value Gap (FVG)

fvg = smc.fvg(ohlc, join_consecutive=False)

Returns per row:

• FVG: 1 (bullish gap) · -1 (bearish gap) · NaN (no gap)
• Top: upper boundary of the gap
• Bottom: lower boundary of the gap
• MitigatedIndex: candle index that closed/filled the gap

Concept:

Bullish FVG:  candle[i-1].high < candle[i+1].low   → gap above
Bearish FVG:  candle[i-1].low  > candle[i+1].high  → gap below

Parameters:

• join_consecutive=True: merges adjacent FVGs → single zone (highest top, lowest bottom)

2. Swing Highs and Lows

swings = smc.swing_highs_lows(ohlc, swing_length=50)

Returns:

• HighLow: 1 (swing high) · -1 (swing low) · NaN
• Level: price of the swing point

Concept:

Swing High: highest high within swing_length candles before AND after
Swing Low:  lowest low  within swing_length candles before AND after

3. Break of Structure (BOS) & Change of Character (CHoCH)

# Requires swing_highs_lows output first
swings = smc.swing_highs_lows(ohlc, swing_length=50)
structure = smc.bos_choch(ohlc, swings, close_break=True)

Returns:

• BOS: 1 (bullish BOS) · -1 (bearish BOS)
• CHOCH: 1 (bullish CHoCH) · -1 (bearish CHoCH)
• Level: price level that was broken
• BrokenIndex: candle that broke the level

close_break parameter:

True  → break confirmed only when candle CLOSES beyond level
False → break on wick touch (high/low crosses level)

BOS vs CHoCH:

BOS   → continuation: structure break in direction of trend
CHoCH → reversal: structure break AGAINST current trend direction

4. Order Blocks (OB)

swings = smc.swing_highs_lows(ohlc, swing_length=50)
ob = smc.ob(ohlc, swings, close_mitigation=False)

Returns:

• OB: 1 (bullish OB) · -1 (bearish OB)
• Top: upper boundary
• Bottom: lower boundary
• OBVolume: sum of current + 2 previous candle volumes
• Percentage: strength = min(highVol, lowVol) / max(highVol, lowVol)

Strength interpretation:

Percentage → 1.0 (100%) = equal bull/bear volume = strongest OB
Percentage → 0.1 (10%)  = highly imbalanced = weaker OB

5. Liquidity

swings = smc.swing_highs_lows(ohlc, swing_length=50)
liq = smc.liquidity(ohlc, swings, range_percent=0.01)

Returns:

• Liquidity: 1 (buy-side) · -1 (sell-side)
• Level: price of liquidity cluster
• End: index of last swing in the cluster
• Swept: index of candle that swept the liquidity

Concept:

Multiple swing highs within range_percent (1%) of each other
→ clustered stops/liquidity pool above = buy-side liquidity
→ price will likely sweep these before reversing

6. Previous High and Low

prev_hl = smc.previous_high_low(ohlc, time_frame="1D")

Returns:

• PreviousHigh · PreviousLow
• BrokenHigh: 1 when price breaks prior period high
• BrokenLow: 1 when price breaks prior period low

Supported timeframes: "15m" · "1H" · "4H" · "1D" · "1W" · "1M"

7. Sessions (Kill Zones)

session = smc.sessions(ohlc, session="London open kill zone",
                        start_time=None, end_time=None, time_zone="UTC")

Built-in sessions:

"Sydney"                    "Tokyo"
"London"                    "New York"
"Asian kill zone"           "London open kill zone"
"New York kill zone"        "london close kill zone"
"Custom"                    → requires start_time + end_time "HH:MM"

Returns:

• Active: 1 if candle is within session · 0 if not
• High: session high so far
• Low: session low so far

8. Retracements

swings = smc.swing_highs_lows(ohlc, swing_length=50)
ret = smc.retracements(ohlc, swings)

Returns:

• Direction: 1 (bullish move) · -1 (bearish move)
• CurrentRetracement%: current retracement from last swing
• DeepestRetracement%: max retracement seen in this move

Complete Usage Example

from smartmoneyconcepts import smc
import pandas as pd

# Load OHLCV data (lowercase columns required)
df = pd.read_csv("EURUSD_H1.csv")
df.columns = ["open", "high", "low", "close", "volume"]

# Step 1: Swing structure (prerequisite for most indicators)
swings = smc.swing_highs_lows(df, swing_length=20)

# Step 2: Market structure
structure = smc.bos_choch(df, swings, close_break=True)

# Step 3: Order blocks
ob = smc.ob(df, swings, close_mitigation=False)

# Step 4: Fair value gaps
fvg = smc.fvg(df, join_consecutive=True)

# Step 5: Liquidity pools
liq = smc.liquidity(df, swings, range_percent=0.005)

# Step 6: Session filter (only trade London open kill zone)
session = smc.sessions(df, session="London open kill zone")

# Step 7: Previous day high/low
prev_hl = smc.previous_high_low(df, time_frame="1D")

# Combine: find bullish OBs that are active during London kill zone
bullish_ob = ob[ob["OB"] == 1]
london_active = session[session["Active"] == 1]
confluence = bullish_ob.index.intersection(london_active.index)
print(f"Bullish OBs during London KZ: {len(confluence)}")

ICT Strategy Pattern: OB + FVG + BOS Confluence

def find_confluences(df, swing_length=20):
    swings  = smc.swing_highs_lows(df, swing_length)
    bos     = smc.bos_choch(df, swings, close_break=True)
    ob      = smc.ob(df, swings)
    fvg     = smc.fvg(df)
    liq     = smc.liquidity(df, swings, range_percent=0.005)

    signals = []
    for i in df.index:
        bullish = (
            ob.loc[i, "OB"] == 1 if i in ob.index else False,      # Bullish OB
            fvg.loc[i, "FVG"] == 1 if i in fvg.index else False,   # Bullish FVG
            bos.loc[i, "BOS"] == 1 if i in bos.index else False,   # BOS up
        )
        if all(bullish):
            signals.append({"index": i, "type": "LONG", "level": ob.loc[i, "Bottom"]})

    return pd.DataFrame(signals)