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7808

Abstract

ation & Plot</li></ul><div id="2ad3"><pre># ADX CALCULATION

def get_adx(high, low, close, lookback): plus_dm = high.diff() minus_dm = low.diff() plus_dm[plus_dm < 0] = 0 minus_dm[minus_dm > 0] = 0

tr1 = pd.DataFrame(high - low)
tr2 = pd.DataFrame(<span class="hljs-built_in">abs</span>(high - close.shift(<span class="hljs-number">1</span>)))
tr3 = pd.DataFrame(<span class="hljs-built_in">abs</span>(low - close.shift(<span class="hljs-number">1</span>)))
frames = [tr1, tr2, tr3]
tr = pd.concat(frames, axis = <span class="hljs-number">1</span>, join = <span class="hljs-string">'inner'</span>).<span class="hljs-built_in">max</span>(axis = <span class="hljs-number">1</span>)
atr = tr.rolling(lookback).mean()

plus_di = <span class="hljs-number">100</span> * (plus_dm.ewm(alpha = <span class="hljs-number">1</span>/lookback).mean() / atr)
minus_di = <span class="hljs-built_in">abs</span>(<span class="hljs-number">100</span> * (minus_dm.ewm(alpha = <span class="hljs-number">1</span>/lookback).mean() / atr))
dx = (<span class="hljs-built_in">abs</span>(plus_di - minus_di) / <span class="hljs-built_in">abs</span>(plus_di + minus_di)) * <span class="hljs-number">100</span>
adx = ((dx.shift(<span class="hljs-number">1</span>) * (lookback - <span class="hljs-number">1</span>)) + dx) / lookback
adx_smooth = adx.ewm(alpha = <span class="hljs-number">1</span>/lookback).mean()
<span class="hljs-keyword">return</span> plus_di, minus_di, adx_smooth

aapl['plus_di'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[0]).rename(columns = {0:'plus_di'}) aapl['minus_di'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[1]).rename(columns = {0:'minus_di'}) aapl['adx'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[2]).rename(columns = {0:'adx'}) aapl = aapl.dropna() aapl.tail()

# ADX PLOT

plot_data = aapl[aapl.index >= '2023-01-03']

ax1 = plt.subplot2grid((11,1), (0,0), rowspan = 5, colspan = 1) ax2 = plt.subplot2grid((11,1), (6,0), rowspan = 5, colspan = 1) ax1.plot(plot_data['close'], linewidth = 2, color = '#ff9800') ax1.set_title('NVIDIA CLOSING PRICE') ax2.plot(plot_data['plus_di'], color = '#26a69a', label = '+ DI 14', linewidth = 3, alpha = 0.3) ax2.plot(plot_data['minus_di'], color = '#f44336', label = '- DI 14', linewidth = 3, alpha = 0.3) ax2.plot(plot_data['adx'], color = '#2196f3', label = 'ADX 14', linewidth = 3) ax2.axhline(35, color = 'grey', linewidth = 2, linestyle = '--') ax2.legend() ax2.set_title('NVIDIA ADX 14') plt.show()</pre></div><figure id="3d67"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*LpL83nm3VrZo7TWKYE2YXA.png"><figcaption>NVIDIA Closing Price vs ADX 14 Indicator</figcaption></figure><ul><li>Step 3: RSI Calculation & Plot</li></ul><div id="c3ce"><pre># RSI CALCULATION

def get_rsi(close, lookback): ret = close.diff() up = [] down = []

<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(ret)):
    <span class="hljs-keyword">if</span> ret[i] &lt; <span class="hljs-number">0</span>:
        up.append(<span class="hljs-number">0</span>)
        down.append(ret[i])
    <span class="hljs-keyword">else</span>:
        up.append(ret[i])
        down.append(<span class="hljs-number">0</span>)

up_series = pd.Series(up)
down_series = pd.Series(down).<span class="hljs-built_in">abs</span>()

up_ewm = up_series.ewm(com = lookback - <span class="hljs-number">1</span>, adjust = <span class="hljs-literal">False</span>).mean()
down_ewm = down_series.ewm(com = lookback - <span class="hljs-number">1</span>, adjust = <span class="hljs-literal">False</span>).mean()

rs = up_ewm/down_ewm
rsi = <span class="hljs-number">100</span> - (<span class="hljs-number">100</span> / (<span class="hljs-number">1</span> + rs))
rsi_df = pd.DataFrame(rsi).rename(columns = {<span class="hljs-number">0</span>:<span class="hljs-string">'rsi'</span>}).set_index(close.index)
rsi_df = rsi_df.dropna()

<span class="hljs-keyword">return</span> rsi_df[<span class="hljs-number">3</span>:]

aapl['rsi_14'] = get_rsi(aapl['close'], 14) aapl = aapl.dropna() aapl.tail()

# RSI PLOT

plot_data = aapl[aapl.index >= '2023-01-03']

Options

olor = 'orange', linewidth = 2.5) ax2.axhline(30, linestyle = '--', linewidth = 1.5, color = 'grey') ax2.axhline(70, linestyle = '--', linewidth = 1.5, color = 'grey') ax2.set_title('NVIDIA RSI 14') plt.show()

# RSI ADX PLOT

plot_data = aapl[aapl.index >= '2023-01-03']

ax1 = plt.subplot2grid((19,1), (0,0), rowspan = 5, colspan = 1) ax2 = plt.subplot2grid((19,1), (7,0), rowspan = 5, colspan = 1) ax3 = plt.subplot2grid((19,1), (14,0), rowspan = 5, colspan = 1)

ax1.plot(plot_data['close'], linewidth = 2.5) ax1.set_title('NVIDIA STOCK PRICES')

ax2.plot(plot_data['rsi_14'], color = 'orange', linewidth = 2.5) ax2.axhline(30, linestyle = '--', linewidth = 1.5, color = 'grey') ax2.axhline(70, linestyle = '--', linewidth = 1.5, color = 'grey') ax2.set_title('NVIDIA RSI 14')

ax3.plot(plot_data['plus_di'], color = '#26a69a', label = '+ DI 14', linewidth = 3, alpha = 0.3) ax3.plot(plot_data['minus_di'], color = '#f44336', label = '- DI 14', linewidth = 3, alpha = 0.3) ax3.plot(plot_data['adx'], color = '#2196f3', label = 'ADX 14', linewidth = 3) ax3.axhline(35, color = 'grey', linewidth = 2, linestyle = '--') ax3.legend() ax3.set_title('NVIDIA ADX 14') plt.show()</pre></div><figure id="0869"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*q6HOIkJOTi8mb1xVB3LMzA.png"><figcaption>NVIDIA Stock Price vs RSI 14 Indicator</figcaption></figure><figure id="7073"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*gP0q_JPQpyBCD8rQHLZFpg.png"><figcaption>NVIDIA Stock Price vs RSI & ADX 14 Indicators</figcaption></figure><ul><li>Step 4: NVIDIA Daily & Cumulative Returns</li></ul><div id="960b"><pre>rets = aapl.close.pct_change().dropna() strat_rets = strategy.adx_rsi_position[1:]*rets plt.title('Daily Returns') rets.plot(color = 'blue', alpha = 0.3, linewidth = 7) plt.show()</pre></div><figure id="972e"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*lmqcRauPPxiTMVMUNVeI7w.png"><figcaption>NVIDIA Daily Returns</figcaption></figure><div id="8ce8"><pre>rets_cum = (1 + rets).cumprod() - 1 strat_cum = (1 + strat_rets).cumprod() - 1

plt.title('Cumulative Returns') rets_cum.plot(color = 'blue', alpha = 0.3, linewidth = 7) plt.show()</pre></div><figure id="aa9a"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*XkOZtGWdKFM2pFOCUCzjxw.png"><figcaption>NVIDIA Cumulative Returns</figcaption></figure><h2 id="2a2a">Conclusions</h2><ul><li>The ADX values ~50+/-5 suggest a very strong trend in Q1'24. However, a decreasing ADX curve indicates that the movement is losing momentum.</li><li>High RSI levels, ~70+/-10, generate sell signals and suggest that NVIDIA is overbought or <a href="https://www.investopedia.com/terms/o/overvalued.asp">overvalued</a> in Q1'24.</li><li>The 1Y stock cumulative returns confirm a reasonable profit (excluding all costs) of the benchmark passive strategy.</li><li>The road ahead: AI-powered stock price forecasting, stochastic simulations, backtesting and benchmarking against S&P 500 in 2024.</li></ul><h2 id="5a33">Explore More</h2><ul><li><a href="https://readmedium.com/algo-trading-nvidia-with-williams-r-macd-indicators-a85dcd70c8cb">Algo-Trading NVIDIA with Williams %R & MACD Indicators</a></li><li><a href="https://readmedium.com/an-algo-trading-sneak-peek-at-top-ai-powered-growth-stocks-1-nvidia-8cbc06c0de5f">An Algo-Trading Sneak Peek at Top AI-Powered Growth Stocks — 1. NVIDIA</a></li><li><a href="https://readmedium.com/useful-fintech-saas-products-guides-blogs-for-quant-trading-b6e65b423b0b">Useful FinTech SaaS Products, Guides & Blogs for Quant Trading</a></li><li><a href="https://wp.me/pdMwZd-7dI">NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading</a></li><li><a href="https://wp.me/pdMwZd-7by">NVIDIA Rolling Volatility: GARCH & XGBoost</a></li><li><a href="https://wp.me/pdMwZd-71E">IQR-Based Log Price Volatility Ranking of Top 19 Blue Chips</a></li></ul><h2 id="593b">References</h2><ul><li><a href="https://readmedium.com/does-combining-adx-and-rsi-create-a-better-profitable-trading-strategy-125a90c36ac">Does Combining ADX and RSI Create a Better Profitable Trading Strategy?</a></li><li><a href="https://tradeciety.com/how-to-choose-the-best-indicators-for-your-trading">HOW TO COMBINE THE BEST INDICATORS AND AVOID WRONG SIGNALS</a></li><li><a href="https://www.tradingview.com/chart/EURUSD/iWijQ5qD-ADX-Trend-Based-Strategies/">ADX Trend-Based Strategies</a></li><li><a href="https://www.quantifiedstrategies.com/rsi-adx-trading-strategy/">RSI & ADX Trading Strategies (Backtest and Rules)</a></li><li><a href="https://www.dukascopy.com/swiss/english/marketwatch/articles/demystifying-the-relative-strength-index-(rsi)-indicator/">DEMYSTIFYING THE RELATIVE STRENGTH INDEX (RSI) INDICATOR</a></li></ul><h2 id="fa2a">Keywords</h2><ul><li>#Python</li><li>#NVDA</li><li>#AI</li><li>#algotrading</li><li>#trading</li><li>#investment</li><li>#fintech</li><li>#finance</li><li>#stockmarket</li><li>#stocks</li></ul><h2 id="9de6">Contacts</h2><ul><li><a href="https://wp.me/PdMwZd-3nr">Website</a></li><li><a href="https://twitter.com/alzapress">Twitter</a></li><li><a href="https://nl.pinterest.com/alexzap922/">Pinterest</a></li></ul></article></body>

Algo-Trading NVIDIA with ADX & RSI Indicators

The objective of this post is to perform technical analysis of the NVIDIA stock price 2023–24 using the ADX-RSI algo-trading algorithm in Python.
The seemingly unstoppable AI chip maker had the best start to the year among the companies in the index. But can it keep going?
Let’s try to answer this question by combining the ADX Trend-Based Strategies with the RSI indicator. Because the ADX measures the intensity of the trend the RSI can help with entries and exits by giving a time based component to the trend.

ADX Indicator

The average directional index (ADX) is a trend indicator that is used to quantify trend strength. ADX values help traders identify the strongest and most profitable trends to trade. To interpret trend momentum with ADX, we will look at the following:
ADX values above 25: This indicates a strong trend. The higher the ADX value, the stronger the trend. ADX values below 25: This shows a weak trend. The lower the ADX value, the weaker the trend.
ADX values increasing: This indicates that the movement is gaining momentum. ADX values decreasing: This indicates that the movement is losing momentum.
Quantifying Trend Strength: 0–25 Absent or Weak Trend, 25–50 Strong Trend, 50–75 Very Strong Trend, and 75–100 Extremely Strong Trend.
The relative strength index (RSI) is a momentum indicator used in stock technical analysis.

RSI Indicator

The RSI is displayed as an oscillator on a scale of 0 to 100.
An asset is usually considered overbought when the RSI is above 70 and oversold when it is below 30.
The RSI line crossing below the overbought line or above oversold line is often seen by traders as a signal to buy or sell.

The ADX-RSI Algorithm

Let’s delve into details of the ADX-RSI algorithm aimed at maximizing the NVIDIA’s ROI/Risk ratio in 2024. Our benchmark is a buy-and-hold passive investment strategy that started on the first trading day of 2023.
Step 1: Importing Python packages and reading real-time stock data

# IMPORTING PACKAGES

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import requests
from math import floor
from termcolor import colored as cl

plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (20,10)

# EXTRACTING STOCK DATA

import pandas as pd
import numpy as np
import requests
import matplotlib.pyplot as plt
from math import floor
from termcolor import colored as cl

plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (20,10)

def get_historical_data(symbol, start_date):
    api_key = 'YOUR API KEY'
    api_url = f'https://api.twelvedata.com/time_series?symbol={symbol}&interval=1day&outputsize=5000&apikey={api_key}'
    raw_df = requests.get(api_url).json()
    df = pd.DataFrame(raw_df['values']).iloc[::-1].set_index('datetime').astype(float)
    df = df[df.index >= start_date]
    df.index = pd.to_datetime(df.index)
    return df

aapl = get_historical_data('NVDA', '2023-01-03')
aapl.tail()
           open      high      low       close     volume
datetime     
2024-03-22 911.40997 947.78003 908.34003 942.89001 58521500.0
2024-03-25 939.40997 967.65997 935.09998 950.02002 55213600.0
2024-03-26 958.51001 963.75000 925.02002 925.60999 51364800.0
2024-03-27 931.12000 932.40002 891.22998 902.50000 58606700.0
2024-03-28 900.00000 913.00000 891.92999 903.56000 43521200.0

Step 2: ADX Calculation & Plot

# ADX CALCULATION

def get_adx(high, low, close, lookback):
    plus_dm = high.diff()
    minus_dm = low.diff()
    plus_dm[plus_dm < 0] = 0
    minus_dm[minus_dm > 0] = 0
    
    tr1 = pd.DataFrame(high - low)
    tr2 = pd.DataFrame(abs(high - close.shift(1)))
    tr3 = pd.DataFrame(abs(low - close.shift(1)))
    frames = [tr1, tr2, tr3]
    tr = pd.concat(frames, axis = 1, join = 'inner').max(axis = 1)
    atr = tr.rolling(lookback).mean()
    
    plus_di = 100 * (plus_dm.ewm(alpha = 1/lookback).mean() / atr)
    minus_di = abs(100 * (minus_dm.ewm(alpha = 1/lookback).mean() / atr))
    dx = (abs(plus_di - minus_di) / abs(plus_di + minus_di)) * 100
    adx = ((dx.shift(1) * (lookback - 1)) + dx) / lookback
    adx_smooth = adx.ewm(alpha = 1/lookback).mean()
    return plus_di, minus_di, adx_smooth

aapl['plus_di'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[0]).rename(columns = {0:'plus_di'})
aapl['minus_di'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[1]).rename(columns = {0:'minus_di'})
aapl['adx'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[2]).rename(columns = {0:'adx'})
aapl = aapl.dropna()
aapl.tail()

# ADX PLOT

plot_data = aapl[aapl.index >= '2023-01-03']

ax1 = plt.subplot2grid((11,1), (0,0), rowspan = 5, colspan = 1)
ax2 = plt.subplot2grid((11,1), (6,0), rowspan = 5, colspan = 1)
ax1.plot(plot_data['close'], linewidth = 2, color = '#ff9800')
ax1.set_title('NVIDIA CLOSING PRICE')
ax2.plot(plot_data['plus_di'], color = '#26a69a', label = '+ DI 14', linewidth = 3, alpha = 0.3)
ax2.plot(plot_data['minus_di'], color = '#f44336', label = '- DI 14', linewidth = 3, alpha = 0.3)
ax2.plot(plot_data['adx'], color = '#2196f3', label = 'ADX 14', linewidth = 3)
ax2.axhline(35, color = 'grey', linewidth = 2, linestyle = '--')
ax2.legend()
ax2.set_title('NVIDIA ADX 14')
plt.show()

NVIDIA Closing Price vs ADX 14 Indicator

Step 3: RSI Calculation & Plot

# RSI CALCULATION

def get_rsi(close, lookback):
    ret = close.diff()
    up = []
    down = []
    
    for i in range(len(ret)):
        if ret[i] < 0:
            up.append(0)
            down.append(ret[i])
        else:
            up.append(ret[i])
            down.append(0)
    
    up_series = pd.Series(up)
    down_series = pd.Series(down).abs()
    
    up_ewm = up_series.ewm(com = lookback - 1, adjust = False).mean()
    down_ewm = down_series.ewm(com = lookback - 1, adjust = False).mean()
    
    rs = up_ewm/down_ewm
    rsi = 100 - (100 / (1 + rs))
    rsi_df = pd.DataFrame(rsi).rename(columns = {0:'rsi'}).set_index(close.index)
    rsi_df = rsi_df.dropna()
    
    return rsi_df[3:]

aapl['rsi_14'] = get_rsi(aapl['close'], 14)
aapl = aapl.dropna()
aapl.tail()

# RSI PLOT

plot_data = aapl[aapl.index >= '2023-01-03']

ax1 = plt.subplot2grid((11,1), (0,0), rowspan = 5, colspan = 1)
ax2 = plt.subplot2grid((11,1), (6,0), rowspan = 5, colspan = 1)
ax1.plot(plot_data['close'], linewidth = 2.5)
ax1.set_title('NVIDIA STOCK PRICES')
ax2.plot(plot_data['rsi_14'], color = 'orange', linewidth = 2.5)
ax2.axhline(30, linestyle = '--', linewidth = 1.5, color = 'grey')
ax2.axhline(70, linestyle = '--', linewidth = 1.5, color = 'grey')
ax2.set_title('NVIDIA RSI 14')
plt.show()

# RSI ADX PLOT

plot_data = aapl[aapl.index >= '2023-01-03']

ax1 = plt.subplot2grid((19,1), (0,0), rowspan = 5, colspan = 1)
ax2 = plt.subplot2grid((19,1), (7,0), rowspan = 5, colspan = 1)
ax3 = plt.subplot2grid((19,1), (14,0), rowspan = 5, colspan = 1)

ax1.plot(plot_data['close'], linewidth = 2.5)
ax1.set_title('NVIDIA STOCK PRICES')

ax2.plot(plot_data['rsi_14'], color = 'orange', linewidth = 2.5)
ax2.axhline(30, linestyle = '--', linewidth = 1.5, color = 'grey')
ax2.axhline(70, linestyle = '--', linewidth = 1.5, color = 'grey')
ax2.set_title('NVIDIA RSI 14')

ax3.plot(plot_data['plus_di'], color = '#26a69a', label = '+ DI 14', linewidth = 3, alpha = 0.3)
ax3.plot(plot_data['minus_di'], color = '#f44336', label = '- DI 14', linewidth = 3, alpha = 0.3)
ax3.plot(plot_data['adx'], color = '#2196f3', label = 'ADX 14', linewidth = 3)
ax3.axhline(35, color = 'grey', linewidth = 2, linestyle = '--')
ax3.legend()
ax3.set_title('NVIDIA ADX 14')
plt.show()

NVIDIA Stock Price vs RSI & ADX 14 Indicators

Step 4: NVIDIA Daily & Cumulative Returns

rets = aapl.close.pct_change().dropna()
strat_rets = strategy.adx_rsi_position[1:]*rets
plt.title('Daily Returns')
rets.plot(color = 'blue', alpha = 0.3, linewidth = 7)
plt.show()

rets_cum = (1 + rets).cumprod() - 1 
strat_cum = (1 + strat_rets).cumprod() - 1

plt.title('Cumulative Returns')
rets_cum.plot(color = 'blue', alpha = 0.3, linewidth = 7)
plt.show()

Conclusions

The ADX values ~50+/-5 suggest a very strong trend in Q1'24. However, a decreasing ADX curve indicates that the movement is losing momentum.
High RSI levels, ~70+/-10, generate sell signals and suggest that NVIDIA is overbought or overvalued in Q1'24.
The 1Y stock cumulative returns confirm a reasonable profit (excluding all costs) of the benchmark passive strategy.
The road ahead: AI-powered stock price forecasting, stochastic simulations, backtesting and benchmarking against S&P 500 in 2024.

Explore More

References

Keywords

#Python
#NVDA
#AI
#algotrading
#trading
#investment
#fintech
#finance
#stockmarket
#stocks