smartmoneyconcepts Python Library

Install the smartmoneyconcepts library using pip to access ICT-inspired financial indicators.

pip install smartmoneyconcepts

The smc module expects a pandas DataFrame containing properly formatted OHLC (Open, High, Low, Close) data.

Requirements:

• Column names must be in lowercase: ["open", "high", "low", "close"].
• For indicators requiring volume, include the "volume" column.

By default, the library prints a credit message upon import. You can suppress this by setting the SMC_CREDIT environment variable to 0 before importing the library.

export SMC_CREDIT=0

Identifies shifts in market structure.

Parameters:

• ohlc (DataFrame): The input OHLC data.
• swing_highs_lows (DataFrame): The DataFrame output from the smc.swing_highs_lows() function.
• close_break (bool): If True, the break is determined by the candle's close. If False, it is determined by the candle's high/low.

Returns (DataFrame columns):

• 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 price level of the BOS or CHoCH.
• BrokenIndex: The index of the candle that broke the level.

Detects price ranges where significant market orders are suspected to exist.

Parameters:

• ohlc (DataFrame): The input OHLC data.
• swing_highs_lows (DataFrame): The DataFrame output from the smc.swing_highs_lows() function.
• close_mitigation (bool): If True, the order block is considered mitigated based on the candle close; otherwise, it uses the high/low.

Returns (DataFrame columns):

• 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 calculated as volume + 2 last volumes amounts.
• Percentage: Strength of the order block (min(highVolume, lowVolume)/max(highVolume,lowVolume)).

Calculates the percentage of retracement from recent swing highs or lows.

Parameters:

• ohlc (DataFrame): The input OHLC data.
• swing_highs_lows (DataFrame): The DataFrame output from the smc.swing_highs_lows() function.

Returns (DataFrame columns):

• 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 recorded from the swing high or low.

smc.retracements(ohlc, swing_highs_lows)

Identifies local price extremes based on a lookback/lookahead window.

Parameters:

• ohlc (DataFrame): The input OHLC data.
• swing_length (int): The number of candles to look at before and after the current candle to determine if it is a swing point.

Returns (DataFrame columns):

• HighLow: 1 if a swing high, -1 if a swing low.
• Level: The price level of the swing high or low.

Retrieves the high and low prices from a specified previous timeframe.

Parameters:

• ohlc (DataFrame): The input OHLC data.
• time_frame (str): The timeframe to reference. Supported values: 15m, 1H, 4H, 1D, 1W, 1M.

Returns (DataFrame columns):

• PreviousHigh: The high of the previous timeframe.
• PreviousLow: The low of the previous timeframe.
• BrokenHigh: 1 if price has broken the previous high, 0 otherwise.
• BrokenLow: 1 if price has broken the previous low, 0 otherwise.

Detects Fair Value Gaps where a price imbalance exists between candles.

Parameters:

• ohlc (DataFrame): The input OHLC data.
• join_consecutive (bool): If True, multiple consecutive FVGs are merged into one using the highest top and lowest bottom.

Returns (DataFrame columns):

• FVG: 1 if bullish FVG, -1 if bearish FVG.
• Top: The top price level of the gap.
• Bottom: The bottom price level of the gap.
• MitigatedIndex: The index of the candle that mitigated the gap.

smc.fvg(ohlc, join_consecutive=False)

Determines which candles fall within specific trading sessions or kill zones.

Parameters:

• ohlc (DataFrame): The input OHLC data.
• session (str): The session name. Options: Sydney, Tokyo, London, New York, Asian kill zone, London open kill zone, New York kill zone, london close kill zone, or Custom.
• start_time (str): Required only for Custom sessions. Format: "HH:MM".
• end_time (str): Required only for Custom sessions. Format: "HH:MM".
• time_zone (str): The timezone of the candles (e.g., "UTC+0" or "GMT+0"). Defaults to "UTC".

Returns (DataFrame columns):

• Active: 1 if the candle is within the session, 0 otherwise.
• High: The highest price point reached during the session.
• Low: The lowest price point reached during the session.

Identifies areas where multiple highs or lows exist within a tight price range.

Parameters:

• ohlc (DataFrame): The input OHLC data.
• swing_highs_lows (DataFrame): The DataFrame output from the smc.swing_highs_lows() function.
• range_percent (float): The percentage of the range used to determine if highs/lows are close enough to be considered liquidity.

Returns (DataFrame columns):

• Liquidity: 1 if bullish liquidity, -1 if bearish liquidity.
• Level: The price level of the liquidity.
• End: The index of the last liquidity level.
• Swept: The index of the candle that swept the liquidity.