Merge branch 'master' of github.com:matplotlib/mplfinance

Author: DanielGoldfarb

RELEASE_NOTES.md

@@ -1,8 +1,12 @@
 ####
 ---
 
-### <a name="v0.12.7a11"></a>v0.12.7a11 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; To Be released 2021-03-30
+### <a name="v0.12.7a12"></a>v0.12.7a12 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; released 2021-04-09
 
+- <a name="v0.12.7a12"></a>v0.12.7a12 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; merged 2021-04-08
+  - Fixed kwarg **`return_calculated_values`**
+    - It was *not* returning all values for `type='pnf'` (point and figure).
+    - See **[stackoverflow 66991998](https://stackoverflow.com/questions/66991998/)** for more information.
 - <a name="v0.12.7a11"></a>v0.12.7a11 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; merged 2021-03-26
   - Prior to this version, **`xlim`** kwarg accepted only float or int:
     - float as matplotlib date; (only when `show_nontrading=True`)

examples/.gitignore

@@ -0,0 +1,2 @@
+*.png
+*.jpg

examples/price-movement_plots.ipynb

@@ -389,7 +389,9 @@ def _valid_pnf_kwargs():
         'box_size'    : { 'Default'     : 'atr',
                           'Validator'   : lambda value: isinstance(value,(float,int)) or value == 'atr' },
         'atr_length'  : { 'Default'     : 14,
-                          'Validator'   : lambda value: isinstance(value,int) or value == 'total' },               
+                          'Validator'   : lambda value: isinstance(value,int) or value == 'total' },
+        'reversal'    : { 'Default'     : 1,
+                          'Validator'   : lambda value: isinstance(value,int) }               
     }
 
     _validate_vkwargs_dict(vkwargs)
@@ -855,7 +857,9 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
                                     edgecolors=edge_colors,
                                     linewidths=lw
                                     )
-    return [rectCollection,], new_dates, new_volumes, brick_values, brick_size
+    calculated_values = dict(dates=new_dates,volumes=new_volumes,
+                             values=brick_values,size=brick_size)
+    return [rectCollection,], calculated_values
 
 
 def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnfig_params, closes, marketcolors=None):
@@ -882,10 +886,11 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     first to ensure every time there is a trend change (ex. previous box is
     an X, current brick is a O) we draw one less box to account for the price 
     having to move the previous box's amount before creating a box in the 
-    opposite direction. Next we adjust volume and dates to combine volume into 
-    non 0 box indexes and to only use dates from non 0 box indexes. We then
-    remove all 0s from the boxes array and once again combine adjacent similarly
-    signed differences in boxes.
+    opposite direction. During this same step we also combine like signed elements 
+    and associated volume/date data ignoring any zero values that are created by 
+    subtracting 1 from the box value. Next we recreate the box array utilizing a 
+    rolling_change and volume_cache to store and sum the changes that don't break 
+    the reversal threshold.
 
     Lastly, we enumerate through the boxes to populate the line_seg and circle_patches
     arrays. line_seg holds the / and \ line segments that make up an X and 
@@ -929,6 +934,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
 
     box_size = pointnfig_params['box_size']
     atr_length = pointnfig_params['atr_length']
+    reversal = pointnfig_params['reversal']
 
     if box_size == 'atr':
         if atr_length == 'total':
@@ -943,6 +949,9 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
         elif box_size < lower_limit:
             raise ValueError("Specified box_size may not be smaller than (0.01* the Average True Value of the dataset) which has value: "+ str(lower_limit))
 
+    if reversal < 1 or reversal > 9:
+        raise ValueError("Specified reversal must be an integer in the range [1,9]")
+    
     alpha  = marketcolors['alpha']
 
     uc     = mcolors.to_rgba(marketcolors['ohlc'][ 'up' ], alpha)
@@ -972,33 +981,88 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     boxes, indexes = combine_adjacent(boxes)
     new_volumes, new_dates = coalesce_volume_dates(temp_volumes, temp_dates, indexes)
 
-    #subtract 1 from the abs of each diff except the first to account for the first box using the last box in the opposite direction
-    first_elem = boxes[0]
-    boxes = [boxes[i]- int((boxes[i]/abs(boxes[i]))) for i in range(1, len(boxes))]
-    boxes.insert(0, first_elem)
-
-    # adjust volume and dates to make sure volume is combined into non 0 box indexes and only use dates from non 0 box indexes
-    temp_volumes, temp_dates = [], []
-    for i in range(len(boxes)):
-        if boxes[i] == 0:
-            volume_cache += new_volumes[i]
-        else:
+    adjusted_boxes = [boxes[0]]
+    temp_volumes, temp_dates = [new_volumes[0]], [new_dates[0]]
+    volume_cache = 0
+
+    # Clean data to subtract 1 from all box # not including the first boxes element and combine like signed adjacent values (after ignoring zeros)
+    for i in range(1, len(boxes)):
+        adjusted_value = boxes[i]- int((boxes[i]/abs(boxes[i])))
+
+        # not equal to 0 and different signs
+        if adjusted_value != 0 and adjusted_boxes[-1]*adjusted_value < 0:
+
+            # Append adjusted_value, volumes, and date to associated lists
+            adjusted_boxes.append(adjusted_value)
             temp_volumes.append(new_volumes[i] + volume_cache)
-            volume_cache = 0
             temp_dates.append(new_dates[i])
-    
-    #remove 0s from boxes
-    boxes = list(filter(lambda diff: diff != 0, boxes))
 
-    # combine adjacent similarly signed differences again after 0s removed
-    boxes, indexes = combine_adjacent(boxes)
-    new_volumes, new_dates = coalesce_volume_dates(temp_volumes, temp_dates, indexes)
+            # reset volume_cache once we use it
+            volume_cache = 0
+
+        # not equal to 0 and same signs
+        elif adjusted_value != 0 and adjusted_boxes[-1]*adjusted_value > 0:
+
+            # Add adjusted_value and volume values to last added elements
+            adjusted_boxes[-1] += adjusted_value
+            temp_volumes[-1] += new_volumes[i] + volume_cache
+
+            # reset volume_cache once we use it
+            volume_cache = 0
+
+        else: # adjusted_value == 0
+            volume_cache += new_volumes[i]
+
+    boxes = [adjusted_boxes[0]]
+    new_volumes = [temp_volumes[0]]
+    new_dates = [temp_dates[0]]
+    
+    rolling_change = 0
+    volume_cache = 0
+    biggest_difference = 0 # only used for the last column
+
+    #Clean data to account for reversal size (added to allow overriding the default reversal of 1)
+    for i in range(1, len(adjusted_boxes)):
+
+        # Add to rolling_change and volume_cache which stores the box and volume values 
+        rolling_change += adjusted_boxes[i]
+        volume_cache += temp_volumes[i]
+
+        # if rolling_change is the same sign as the previous box and the abs value is bigger than the 
+        # abs value of biggest_difference then we should replace biggest_difference with rolling_change
+        if rolling_change*boxes[-1] > 0 and abs(rolling_change) > abs(biggest_difference):
+            biggest_difference = rolling_change
+
+        # Add to new list if the rolling change is >= the reversal
+        if abs(rolling_change) >= reversal:
+
+            # if rolling_change is the same sign as the previous # of boxes then combine
+            if rolling_change*boxes[-1] > 0:
+                boxes[-1] += rolling_change
+                new_volumes[-1] += volume_cache
+
+            # otherwise add new box
+            else: # < 0 (== 0 can't happen since neither rolling_change or boxes[-1] can be 0)
+                boxes.append(rolling_change)
+                new_volumes.append(volume_cache)
+                new_dates.append(temp_dates[i])
+            
+            # reset rolling_change and volume_cache once we've used them
+            rolling_change = 0
+            volume_cache = 0
+            
+            # reset biggest_difference as we start from the beginning every time there is a reversal
+            biggest_difference = 0
+    
+    # Adjust the last box column if the left over rolling_change is the same sign as the column
+    boxes[-1] += biggest_difference
+    new_volumes[-1] += volume_cache
 
     curr_price = closes[0]
     box_values = [] # y values for the boxes
     circle_patches = [] # list of circle patches to be used to create the cirCollection
     line_seg = [] # line segments that make up the Xs
-    
+
     for index, difference in enumerate(boxes):
         diff = abs(difference)
 
@@ -1007,9 +1071,9 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
 
         x = [index] * (diff)
         y = [curr_price + (i * box_size * sign) for i in range(start_iteration, diff+start_iteration)]
-        
+
         curr_price += (box_size * sign * (diff))
-        box_values.append(sum(y) / len(y))
+        box_values.append( y )
 
         for i in range(len(x)): # x and y have the same length
             height = box_size * 0.85
@@ -1036,7 +1100,9 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
                                  linewidths=lw,
                                  antialiaseds=useAA
                                  )
-    return [cirCollection, xCollection], new_dates, new_volumes, box_values, box_size
+    calculated_values = dict(dates=new_dates,counts=boxes,values=box_values,
+                             volumes=new_volumes,size=box_size)
+    return [cirCollection, xCollection], calculated_values
 
 
 def _construct_aline_collections(alines, dtix=None):

examples/savefig.ipynb

@@ -1,5 +1,5 @@
 
-version_info = (0, 12, 7, 'alpha', 12)
+version_info = (0, 12, 7, 'alpha', 15)
 
 _specifier_ = {'alpha': 'a','beta': 'b','candidate': 'rc','final': ''}
 

examples/scratch_pad/pnf_reversal.ipynb

@@ -7,6 +7,7 @@
 import numpy  as np
 import copy
 import io
+import os
 import math
 import warnings
 import statistics as stat
@@ -168,7 +169,7 @@ def _valid_plot_kwargs():
                                         'Validator'   : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) },
 
         'savefig'                   : { 'Default'     : None, 
-                                        'Validator'   : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) },
+                                        'Validator'   : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) or isinstance(value, os.PathLike) },
 
         'block'                     : { 'Default'     : None, 
                                         'Validator'   : lambda value: isinstance(value,bool) },
@@ -400,16 +401,25 @@ def plot( data, **kwargs ):
         collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style)
 
     if ptype in VALID_PMOVE_TYPES:
-        collections, new_dates, volumes, brick_values, size = collections
-        formatter = IntegerIndexDateTimeFormatter(new_dates, fmtstring)
-        xdates = np.arange(len(new_dates))
+        collections, calculated_values = collections
+        volumes       = calculated_values['volumes']
+        pmove_dates   = calculated_values['dates']
+        pmove_values  = calculated_values['values']
+        if all([isinstance(v,(list,tuple)) for v in pmove_values]):
+            pmove_avgvals = [sum(v)/len(v) for v in pmove_values]
+        else:
+            pmove_avgvals = pmove_values
+        pmove_size    = calculated_values['size']
+        pmove_counts  = calculated_values['counts'] if 'counts' in calculated_values else None
+        formatter = IntegerIndexDateTimeFormatter(pmove_dates, fmtstring)
+        xdates = np.arange(len(pmove_dates))
 
     if collections is not None:
         for collection in collections:
             axA1.add_collection(collection)
 
     if ptype in VALID_PMOVE_TYPES:
-        mavprices = _plot_mav(axA1,config,xdates,brick_values)
+        mavprices = _plot_mav(axA1,config,xdates,pmove_avgvals)
     else:
         mavprices = _plot_mav(axA1,config,xdates,closes)
 
@@ -428,8 +438,8 @@ def plot( data, **kwargs ):
         _lows  = lows
         _highs = highs
     else:
-        _lows  = brick_values
-        _highs = [brick+size for brick in brick_values]
+        _lows  = pmove_avgvals
+        _highs = [value+pmove_size for value in pmove_avgvals]
 
     miny = np.nanmin(_lows)
     maxy = np.nanmax(_highs)
@@ -458,13 +468,18 @@ def plot( data, **kwargs ):
 
     if config['return_calculated_values'] is not None:
         retdict = config['return_calculated_values']
-        if ptype in VALID_PMOVE_TYPES:
-            prekey = ptype
-            retdict[prekey+'_bricks'] = brick_values
-            retdict[prekey+'_dates'] = mdates.num2date(new_dates)
-            retdict[prekey+'_size'] = size
-            if config['volume']:
-                retdict[prekey+'_volumes'] = volumes
+        if ptype == 'renko':
+            retdict['renko_bricks' ] = pmove_values
+            retdict['renko_dates'  ] = mdates.num2date(pmove_dates)
+            retdict['renko_size'   ] = pmove_size
+            retdict['renko_volumes'] = volumes if config['volume'] else None
+        elif ptype == 'pnf':
+            retdict['pnf_dates'    ] = mdates.num2date(pmove_dates)
+            retdict['pnf_counts'   ] = pmove_counts
+            retdict['pnf_values'   ] = pmove_values
+            retdict['pnf_avgvals'  ] = pmove_avgvals
+            retdict['pnf_size'     ] = pmove_size
+            retdict['pnf_volumes'  ] = volumes if config['volume'] else None
         if config['mav'] is not None:
             mav = config['mav']
             if len(mav) != len(mavprices):
@@ -480,7 +495,7 @@ def plot( data, **kwargs ):
     # Note: these are NOT mutually exclusive, so the order of this
     #       if/elif is important: VALID_PMOVE_TYPES must be first.
     if ptype in VALID_PMOVE_TYPES:
-        dtix = pd.DatetimeIndex([dt for dt in mdates.num2date(new_dates)])
+        dtix = pd.DatetimeIndex([dt for dt in mdates.num2date(pmove_dates)])
     elif not config['show_nontrading']:
         dtix = data.index
     else: