Merge pull request #12 from joshyattridge/clean_up_returned_data

Major Improvements and Structure changes

Author: joshyattridge

README.md

@@ -2,6 +2,8 @@
 
 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 built using Python, a versatile programming language known for its data analysis and visualization capabilities.
 
+![alt text](https://github.com/joshyattridge/smart-money-concepts/blob/21656dd807c4077f345b6cbf29b1bc37672628e9/tests/test_binance.png)
+
 ## Installation
 
 ```bash
@@ -20,35 +22,100 @@ smc expects properly formated ohlc DataFrame, with column names in lowercase: ["
 
 ## Indicators
 
-- FVG - Fair Value Gap
-- Highs and Lows
-- Swing Tops and Bottoms
-- BOS and CHoCH
-- OB - Order Block
-- VOB - Volumized Order Blocks
-- Liquidity
+### Fair Value Gap (FVG)
+
+```python
+smc.fvg(ohlc)
+```
+
+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.
 
-## Examples
+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
 
-Please take a look at smc.test.py for more detailed examples on how each indicator works.
+### Swing Highs and Lows
 
 ```python
-    smc.fvg(ohlc) # Fair Value Gap
-    smc.highs_lows(ohlc) # Highs and Lows
-    smc.swing_tops_bottoms(ohlc, swing_length=10) # Swing Tops and Bottoms
-    smc.bos_choch(ohlc, close_break=True, filter_liquidity=False) # Detect BOS and CHoCH
-    smc.ob(ohlc) # Order Block
-    smc.vob(ohlc) # Volumized Order Blocks
-    smc.liquidity(ohlc) # Liquidity
+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)
+
+```python
+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)
+
+```python
+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
+
+```python
+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
+
 ## Contributing
 
 This project is still in BETA so please feel free to contribute to the project. By creating your own indicators or improving the existing ones.
 
 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).
-5. Commit your changes (git commit -am 'Add some feature').
-6. Push to the branch (git push origin my-new-feature).
-7. Create a new Pull Request.
+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.

setup.py

@@ -2,7 +2,7 @@
 import codecs
 import os
 
-VERSION = '0.0.14'
+VERSION = '0.0.15'
 DESCRIPTION = 'Getting indicators based on smart money concepts or ICT'
 
 # read the contents of the README file
@@ -18,7 +18,7 @@
     long_description_content_type="text/markdown",
     long_description=LONG_DESCRIPTION,
     packages=["smartmoneyconcepts"],
-    install_requires=["pandas==2.0.2", "numpy==1.24.3", "zigzag", "finta==1.3"],
+    install_requires=["pandas==2.0.2", "numpy==1.24.3"],
     keywords=['smart', 'money', 'concepts', 'ict', 'indicators', 'trading', 'forex', 'stocks', 'crypto', 'order', 'blocks', 'liquidity'],
     url="https://github.com/joshyattridge/smartmoneyconcepts",
     classifiers=[