make necessary changes after reversion

Author: andrewrgarcia

src/mplfinance/plotting.py

@@ -532,8 +532,10 @@ def plot( data, **kwargs ):
 
     if ptype in VALID_PMOVE_TYPES:
         mavprices = _plot_mav(axA1,config,xdates,pmove_avgvals)
+        emaprices = _plot_ema(axA1, config, xdates, pmove_avgvals)
     else:
         mavprices = _plot_mav(axA1,config,xdates,closes)
+        emaprices = _plot_ema(axA1, config, xdates, closes)
 
     avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates))
     if not config['tight_layout']:
@@ -599,6 +601,13 @@ def plot( data, **kwargs ):
             else:
                 for jj in range(0,len(mav)):     
                     retdict['mav' + str(mav[jj])] = mavprices[jj]
+        if config['ema'] is not None:
+            ema = config['ema']
+            if len(ema) != len(emaprices):
+                warnings.warn('len(ema)='+str(len(ema))+' BUT len(emaprices)='+str(len(emaprices)))
+            else:
+                for jj in range(0, len(ema)):
+                    retdict['ema' + str(ema[jj])] = emaprices[jj]
         retdict['minx'] = minx
         retdict['maxx'] = maxx
         retdict['miny'] = miny
@@ -1180,13 +1189,13 @@ def _plot_ema(ax,config,xdates,prices,apmav=None,apwidth=None):
             mean = pd.Series(prices).ewm(span=mav,adjust=False).mean()
             if shift is not None:
                 mean = mean.shift(periods=shift[idx])
-            mavprices = mean.values
+            emaprices = mean.values
             lw = config['_width_config']['line_width']
             if mavc:
-                ax.plot(xdates, mavprices, linewidth=lw, color=next(mavc))
+                ax.plot(xdates, emaprices, linewidth=lw, color=next(mavc))
             else:
-                ax.plot(xdates, mavprices, linewidth=lw)
-            mavp_list.append(mavprices)
+                ax.plot(xdates, emaprices, linewidth=lw)
+            mavp_list.append(emaprices)
     return mavp_list
 
 

tests/test_ema.py

@@ -0,0 +1,79 @@
+
+import mplfinance as mpf
+import requests        # for making http requests to binance
+import json            # for parsing what binance sends back to us
+import pandas as pd    # for storing and manipulating the data we get back
+import numpy as np     # numerical python, i usually need this somewhere
+# and so i import by habit nowadays
+
+import matplotlib.pyplot as plt  # for charts and such
+import datetime as dt  # for dealing with times
+
+INTERVAL = '1d'
+
+
+def get_bars(quote, interval=INTERVAL):
+
+    root_url = 'https://api.binance.com/api/v1/klines'
+    url = root_url + '?symbol=' + quote + '&interval=' + interval
+    data = json.loads(requests.get(url).text)
+    df = pd.DataFrame(data)
+    df.columns = ['open_time',
+                  'o', 'h', 'l', 'c', 'v',
+                  'close_time', 'qav', 'num_trades',
+                  'taker_base_vol', 'taker_quote_vol', 'ignore'
+    ]
+
+    df.index = [dt.datetime.fromtimestamp(x/1000.0) for x in df.close_time]
+
+    return df
+
+
+def coinpair(quote, interval='1d', base='USDT'):
+    '''returns ohlc data of the quote cryptocurrency with
+        the base currency (i.e. 'market'); base for alts must be either USDT or BTC'''
+
+    btcusd = 1 if quote == 'BTC' else \
+                            get_bars('BTCUSDT', interval=interval)['c'].astype('float') \
+                            if base == 'USDT' else 1
+
+    base0 = 'USDT' if quote == 'BTC' else 'BTC'
+
+    df = get_bars(quote + base0, interval=interval)
+
+    df['close'] = df['c'].astype('float')*btcusd
+    df['open' ] = df['o'].astype('float')*btcusd
+    df['high' ] = df['h'].astype('float')*btcusd
+    df['low'  ] = df['l'].astype('float')*btcusd
+
+    df.drop(['o', 'h', 'l', 'c'], axis=1, inplace=True)
+    print(quote, base, 'on {} candles'.format(interval))
+
+    return df
+
+
+def test_ema():
+
+    coin = 'BTC'
+    market = 'USDT'
+    candles = '1M'
+
+    df = coinpair(coin, interval=candles, base=market)
+
+    ema25 = df['close'].ewm(span=25.0, adjust=False).mean()
+    mav25 = df['close'].rolling(window=25).mean()
+
+    ap = [
+        mpf.make_addplot(df, panel=1, type='ohlc', color='c',
+                         ylabel='mpf mav', mav=25, secondary_y=False),
+        mpf.make_addplot(ema25, panel=2, type='line', width=2,  color='c',
+                         ylabel='calculated', secondary_y=False),
+        mpf.make_addplot(mav25, panel=2, type='line', width=2, color='blue',
+                         ylabel='calculated', secondary_y=False)
+    ]
+
+    mpf.plot(df, ylabel="mpf ema", type='ohlc',
+             ema=25, addplot=ap, panel_ratios=(1, 1))
+
+
+test_ema()