Smart Money Concepts (smc)

The Smart Money Concepts Python Indicator is a sophisticated financial tool developed for traders and investors to gain insights into market sentiment, trends, and potential reversals. This indicator is inspired by Inner Circle Trader (ICT) concepts like Order blocks, Liquidity, Fair Value Gap, Swing Highs and Lows, Break of Structure, Change of Character, and more. Please Take a look and contribute to the project.

Installation

pip install smartmoneyconcepts

Usage

from smartmoneyconcepts import smc

Prepare data to use with smc:

smc expects properly formated ohlc DataFrame, with column names in lowercase: ["open", "high", "low", "close"] and ["volume"] for indicators that expect ohlcv input.

Indicators

Fair Value Gap (FVG)

smc.fvg(ohlc, join_consecutive=False)

A fair value gap is when the previous high is lower than the next low if the current candle is bullish. Or when the previous low is higher than the next high if the current candle is bearish.

parameters:

join_consecutive: bool - if there are multiple FVG in a row then they will be merged into one using the highest top and the lowest bottom

returns:

FVG = 1 if bullish fair value gap, -1 if bearish fair value gap

Top = the top of the fair value gap

Bottom = the bottom of the fair value gap

MitigatedIndex = the index of the candle that mitigated the fair value gap

Swing Highs and Lows

smc.swing_highs_lows(ohlc, swing_length = 50)

A swing high is when the current high is the highest high out of the swing_length amount of candles before and after. A swing low is when the current low is the lowest low out of the swing_length amount of candles before and after.

parameters:

swing_length: int - the amount of candles to look back and forward to determine the swing high or low

returns:

HighLow = 1 if swing high, -1 if swing low

Level = the level of the swing high or low

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

smc.bos_choch(ohlc, swing_highs_lows, close_break = True)

These are both indications of market structure changing

parameters:

swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function

close_break: bool - if True then the break of structure will be mitigated based on the close of the candle otherwise it will be the high/low.

returns:

BOS = 1 if bullish break of structure, -1 if bearish break of structure

CHOCH = 1 if bullish change of character, -1 if bearish change of character

Level = the level of the break of structure or change of character

BrokenIndex = the index of the candle that broke the level

Order Blocks (OB)

smc.ob(ohlc, swing_highs_lows, close_mitigation = False)

This method detects order blocks when there is a high amount of market orders exist on a price range.

parameters:

swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function

close_mitigation: bool - if True then the order block will be mitigated based on the close of the candle otherwise it will be the high/low.

returns:

OB = 1 if bullish order block, -1 if bearish order block

Top = top of the order block

Bottom = bottom of the order block

OBVolume = volume + 2 last volumes amounts

Percentage = strength of order block (min(highVolume, lowVolume)/max(highVolume,lowVolume))

Liquidity

smc.liquidity(ohlc, swing_highs_lows, range_percent = 0.01)

Liquidity is when there are multiply highs within a small range of each other. or multiply lows within a small range of each other.

parameters:

swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function

range_percent: float - the percentage of the range to determine liquidity

returns:

Liquidity = 1 if bullish liquidity, -1 if bearish liquidity

Level = the level of the liquidity

End = the index of the last liquidity level

Swept = the index of the candle that swept the liquidity

Previous High And Low

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

This method returns the previous high and low of the given time frame.

parameters:

time_frame: str - the time frame to get the previous high and low 15m, 1H, 4H, 1D, 1W, 1M

returns:

PreviousHigh = the previous high

PreviousLow = the previous low

Sessions

smc.sessions(ohlc, session, start_time, end_time, time_zone = "UTC")

This method returns which candles are within the session specified

parameters:

session: str - the session you want to check (Sydney, Tokyo, London, New York, Asian kill zone, London open kill zone, New York kill zone, london close kill zone, Custom)

start_time: str - the start time of the session in the format "HH:MM" only required for custom session.

end_time: str - the end time of the session in the format "HH:MM" only required for custom session.

time_zone: str - the time zone of the candles can be in the format "UTC+0" or "GMT+0"

returns:

Active = 1 if the candle is within the session, 0 if not

High = the highest point of the session

Low = the lowest point of the session

Retracements

smc.retracements(ohlc, swing_highs_lows)

This method returns the percentage of a retracement from the swing high or low

parameters:

swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function

returns:

Direction = 1 if bullish retracement, -1 if bearish retracement

CurrentRetracement% = the current retracement percentage from the swing high or low

DeepestRetracement% = the deepest retracement percentage from the swing high or low

Hide Credit Message

export SMC_CREDIT=0

This method will hide the credit message when you first import the library.

Contributing

Please feel free to contribute to the project. By creating your own indicators or improving the existing ones. If you are stuggling to find something to do then please check out the issues tab for requested changes.

1. Fork it (https://github.com/joshyattridge/smartmoneyconcepts/fork).
2. Study how it's implemented.
3. Create your feature branch (git checkout -b my-new-feature).
4. Commit your changes (git commit -am 'Add some feature').
5. Push to the branch (git push origin my-new-feature).
6. Create a new Pull Request.

Less is more – each pull request should be minimal, focusing on a single function or a small feature. Large, sweeping changes will not be merged, as they are harder to review and maintain. Keep it simple and focused!

Disclaimer

This project is for educational purposes only. Do not use this indicator as a sole decision maker for your trades. Always use proper risk management and do your own research before making any trades. The author of this project is not responsible for any losses you may incur.