Technical indicators Force Index (FI)

Background

The force index (FI) is a technical indicator to illustrate how strong the actual buying or selling pressure is. High positive values mean there is a strong rising trend, and low values signify a strong downward trend.

The FI is calculated by multiplying the difference between the last and previous closing prices by the volume of the security, yielding a momentum scaled by the volume. The strength of the force is determined by a larger price change or by a larger volume.

The index is an oscillator, fluctuating between positive and negative territory, therefore it is used for trend and breakout confirmation, as well as spotting potential turning points by looking for divergences.

The index is a lagging indicator whch is using prior price and volume data, and then that data is used to calculate an average (EMA), it could then sometimes be slow to provide trade signals. For example, it may take a couple of periods for the force index to start rallying after an upside breakout, but by this time the price may have already moved significantly beyond the breakout point and may thus no longer justify an entry.

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

5 rows × 23 columns

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

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

5 rows × 22 columns

import pandas_ta

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

# compute FI

def compute_efi(stock_data):
    efi = pandas_ta.efi(close=stock_data['Close'],
                        volume=stock_data['Volume'],
                        length=13)
    return efi

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

		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	efi
Date	Ticker
2024-04-25	XYL	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	3.009081e+05
	YUM	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	7.056074e+05
	ZBH	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	-5.940560e+05
	ZBRA	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	1.337205e+06
	ZTS	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	-4.281032e+06

# 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 EFI on the second chart
ax2.plot(aapl_new['efi'], color='orange', linewidth=1)
ax2.set_title("AAPL Elder's Force Index")

Text(0.5, 1.0, "AAPL Elder's Force Index")

Reference

Force Index: Overview, Formulas, Limitations by Cory Mitchell on Investopedia