Technical indicators Ease of Movement (EMV)

Background

Ease of movement (EMV) is a technical analysis indicator that is related to an asset’s price change to its volume. High positive values indicate the price is increasing on low volume, strong negative values indicate the price is dropping on low volume. The moving average of the indicator can be added to act as a trigger line, which is similar to other indicators like MACD.

Theoretically, if prices move easily, they will continue to do so for a period of time that can be traded effectively. The EMV indicator fluctuates around a zero-line. When the indicator is above the line, in positive territory, prices are advancing with relative ease - the greater the value the greater the “ease”. Similarly, when the indicator is negative, prices are decling with relative ease depending on how negative.

Python Implementation

import pandas as pd
import sqlite3

# load S&P500 data from stored database
sp500_db = sqlite3.connect(database="sp500_data.sqlite")

df = pd.read_sql_query(sql="SELECT * FROM SP500",
                       con=sp500_db,
                       parse_dates={"Date"})
df.head()

	index	Date	Ticker	level_0	Adj Close	Close	High	Low	Open	Volume	...	atr	macd	macd signal	bb_low	bb_mid	bb_high	sma	ema	ad	obv
0	0	2014-04-29	A	0	35.049534	38.125893	38.304722	37.174534	38.118740	4688612.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.204858e+06	4688612.0
1	1	2014-04-29	AAL	1	33.476742	35.509998	35.650002	34.970001	35.200001	8994200.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.290623e+06	8994200.0
2	2	2014-04-29	AAPL	2	18.633333	21.154642	21.285000	21.053928	21.205000	337377600.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-4.328196e+07	337377600.0
3	3	2014-04-29	ABBV	3	34.034985	51.369999	51.529999	50.759998	50.939999	5601300.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.273491e+06	5601300.0
4	4	2014-04-29	ABT	4	31.831518	38.540001	38.720001	38.259998	38.369999	4415600.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	9.599290e+05	4415600.0

5 rows × 22 columns

# remove irrelevant columns
df = df.drop('index', axis=1)
#df = df.drop('level_0', axis=1)
df.head()

	Date	Ticker	level_0	Adj Close	Close	High	Low	Open	Volume	garmin_klass_vol	...	atr	macd	macd signal	bb_low	bb_mid	bb_high	sma	ema	ad	obv
0	2014-04-29	A	0	35.049534	38.125893	38.304722	37.174534	38.118740	4688612.0	-0.002274	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.204858e+06	4688612.0
1	2014-04-29	AAL	1	33.476742	35.509998	35.650002	34.970001	35.200001	8994200.0	-0.000788	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.290623e+06	8994200.0
2	2014-04-29	AAPL	2	18.633333	21.154642	21.285000	21.053928	21.205000	337377600.0	-0.006397	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-4.328196e+07	337377600.0
3	2014-04-29	ABBV	3	34.034985	51.369999	51.529999	50.759998	50.939999	5601300.0	-0.062705	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.273491e+06	5601300.0
4	2014-04-29	ABT	4	31.831518	38.540001	38.720001	38.259998	38.369999	4415600.0	-0.013411	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	9.599290e+05	4415600.0

5 rows × 21 columns

import pandas_ta

df = df.set_index(['Date','Ticker'])

# compute EMV

def compute_emv(stock_data):
    emv = pandas_ta.eom(high=stock_data['High'],
                        low=stock_data['Low'],
                        close=stock_data['Close'],
                        volume=stock_data['Volume'],
                        length=14)
    return emv

df['emv'] = df.groupby(level=1, group_keys=False).apply(compute_emv)
df.tail()

		level_0	Adj Close	Close	High	Low	Open	Volume	garmin_klass_vol	rsi	atr	macd	macd signal	bb_low	bb_mid	bb_high	sma	ema	ad	obv	emv
Date	Ticker
2024-04-25	XYL	1232347	130.610001	130.610001	131.199997	128.100006	129.619995	963600.0	0.000264	61.482745	0.665306	0.355146	0.255276	4.845591	4.863502	4.881413	128.482000	128.582695	1.381747e+08	42359600.0	-1.275613
	YUM	1232348	141.559998	141.559998	142.169998	140.389999	141.979996	1693100.0	0.000076	65.668163	0.322566	0.550830	0.285784	4.913482	4.937968	4.962454	138.497000	138.658563	8.902989e+07	253995207.0	47.014878
	ZBH	1232349	119.750000	119.750000	121.349998	118.769997	120.709999	1078800.0	0.000206	35.636078	-0.350196	-0.889200	-0.646480	4.772846	4.835888	4.898931	125.012999	123.565569	7.870077e+07	-62096220.0	-114.852947
	ZBRA	1232350	292.529999	292.529999	293.290009	271.630005	274.359985	674700.0	0.001355	56.172272	0.500501	-0.391439	-0.242456	5.588730	5.666387	5.744044	288.205502	284.262362	5.349014e+07	37474700.0	-2359.322092
	ZTS	1232351	153.360001	153.360001	153.589996	150.039993	150.970001	4567200.0	0.000178	39.809619	1.374957	-3.202379	-3.584353	4.967408	5.065065	5.162723	157.579352	157.003521	2.636284e+08	255609600.0	-95.499616

# update the database
df = df.reset_index()
df.to_sql(name="SP500",
          con=sp500_db,
          if_exists="replace",
          index=True)
sp500_db.close()

import matplotlib.pyplot as plt
from datetime import datetime

# select AAPL
aapl = df[df['Ticker'] == 'AAPL'].set_index('Date')

# only select the data from 2022-01-01
aapl_new = aapl[aapl.index > datetime(2022,1,1)]

# set the theme of the chart
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (20,16)

# create two charts on the same figure
ax1 = plt.subplot2grid((10,1),(0,0), rowspan=4, colspan=1)
ax2 = plt.subplot2grid((10,1),(5,0), rowspan=4, colspan=1)

# plot the closing price on the first chart
ax1.plot(aapl_new['Adj Close'])
ax1.set_title('AAPL Adjust Close Price')

# plot the EMV on the second chart
ax2.plot(aapl_new['emv'], color='orange', linewidth=1)
ax2.set_title('AAPL Ease of Movement')

Text(0.5, 1.0, 'AAPL Ease of Movement')

Reference

Ease of Movement Indicator: Overview, Formula, FAQ by Gordon Scott on Investopedia