Register & Get Data<\/span><\/a><\/p>\n\n\n\nStep-3: Extracting Historical Data<\/h3>\n\n\n\n Before heading into the extraction part, it is first essential to have some background about historical or end-of-day data. In a nutshell, historical data consists of information accumulated over a period of time. It helps in identifying patterns and trends in the data. It also assists in studying market behavior. Now, you can easily extract the historical data of any tradeable assets using the eod<\/code> package by following this code:<\/p>\n\n\n\n \n
\n \n
# EXTRACTING HISTORICAL DATA\n\ndef get_historical_data(ticker, start_date):\n json_resp = client.get_eod_historical_stock_market_data(symbol = ticker, period = 'd', from_date = start_date, order = 'a')\n df = pd.DataFrame(json_resp)\n df = df.set_index('date')\n df.index = pd.to_datetime(df.index)\n return df\n\naapl = get_historical_data('AAPL', '2020-01-01')\naapl.tail()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n In the above code, we are using the get_eod_historical_stock_market_data<\/code> function provided by the eodhd<\/code> package to extract the split-adjusted historical stock data of Apple. The function consists of the following parameters:<\/p>\n\n\n\n\nthe ticker<\/code> parameter where the symbol of the stock we are interested in extracting the data should be mentioned<\/li>\n\n\n\nthe period<\/code> refers to the time interval between each data point (one-day interval in our case).<\/li>\n\n\n\nthe from_date<\/code> and to_date<\/code> parameters which indicate the starting and ending date of the data respectively. The format of the input should be \u201cYYYY-MM-DD\u201d<\/li>\n\n\n\nthe order<\/code> parameter which is an optional parameter that can be used to order the dataframe either in ascending (a<\/code>) or descending (d<\/code>). It is ordered based on the dates.<\/li>\n<\/ul>\n\n\n\nAfter extracting the historical data, we are performing some data-wrangling processes to clean and format the data. The final dataframe looks like this:<\/p>\n\n\n\nStep-4: ADX Calculation<\/h3>\n\n\n\n In this step, we are going to calculate the values of ADX by following the method we discussed before.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
def get_adx(high, low, close, lookback):\n plus_dm = high.diff()\n minus_dm = low.diff()\n plus_dm[plus_dm < 0] = 0\n minus_dm[minus_dm > 0] = 0\n \n tr1 = pd.DataFrame(high - low)\n tr2 = pd.DataFrame(abs(high - close.shift(1)))\n tr3 = pd.DataFrame(abs(low - close.shift(1)))\n frames = [tr1, tr2, tr3]\n tr = pd.concat(frames, axis = 1, join = 'inner').max(axis = 1)\n atr = tr.rolling(lookback).mean()\n \n plus_di = 100 * (plus_dm.ewm(alpha = 1\/lookback).mean() \/ atr)\n minus_di = abs(100 * (minus_dm.ewm(alpha = 1\/lookback).mean() \/ atr))\n dx = (abs(plus_di - minus_di) \/ abs(plus_di + minus_di)) * 100\n adx = ((dx.shift(1) * (lookback - 1)) + dx) \/ lookback\n adx_smooth = adx.ewm(alpha = 1\/lookback).mean()\n return plus_di, minus_di, adx_smooth\n\naapl['plus_di'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[0]).rename(columns = {0:'plus_di'})\naapl['minus_di'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[1]).rename(columns = {0:'minus_di'})\naapl['adx'] = pd.DataFrame(get_adx(aapl['high'], aapl['low'], aapl['close'], 14)[2]).rename(columns = {0:'adx'})\naapl = aapl.dropna()\naapl.tail()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\nCode Explanation:<\/strong> We are first defining a function named \u2018get_adx\u2019 that takes a stock\u2019s high (\u2018high\u2019), low (\u2018low\u2019), and close data (\u2018close\u2019) along with the lookback period (\u2018lookback\u2019) as parameters.<\/p>\n\n\n\nInside the function, we are first calculating and storing the + DM and – DM into the \u2018plus_dm\u2019 and \u2018minus_dm\u2019 respectively. Then comes the ATR calculation where we are first calculating the three differences and defined a variable \u2018tr\u2019 to store the highest values among the determined differences, then, we calculated and stored the values of ATR into the \u2018atr\u2019 variable.<\/p>\n\n\n\n
Using the calculated directional movements and ATR values, we are calculating the + DI and – DI and stored them into the \u2018plus_di\u2019 and \u2018minus_di\u2019 variables respectively. With the help of the previously discussed formula, we are calculating the Directional Index values and stored them into the \u2018dx\u2019 variable, and applied those values into the ADX formula to calculate the Average Directional Index values. Then, we defined a variable \u2018adx_smooth\u2019 to store the smoothed values of ADX. Finally, we are returning and calling the function to obtain the + DI, – DI, and ADX values of Apple with 14 as the lookback period.<\/p>\n\n\n\n
Step-5: ADX Plot<\/h3>\n\n\n\n In this step, we are going to plot the calculated ADX values of Apple to make more sense out of it. The main aim of this part is not on the coding section but instead to observe the plot to gain a solid understanding of the Average Directional Index.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
ax1 = plt.subplot2grid((11,1), (0,0), rowspan = 5, colspan = 1)\nax2 = plt.subplot2grid((11,1), (6,0), rowspan = 5, colspan = 1)\nax1.plot(aapl['close'], linewidth = 2, color = '#ff9800')\nax1.set_title('AAPL CLOSING PRICE')\nax2.plot(aapl['plus_di'], color = '#26a69a', label = '+ DI 14', linewidth = 3, alpha = 0.3)\nax2.plot(aapl['minus_di'], color = '#f44336', label = '- DI 14', linewidth = 3, alpha = 0.3)\nax2.plot(aapl['adx'], color = '#2196f3', label = 'ADX 14', linewidth = 3)\nax2.axhline(25, color = 'grey', linewidth = 2, linestyle = '--')\nax2.legend()\nax2.set_title('AAPL ADX 14')\nplt.show()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\nThe above chart is divided into two panels: the upper panel with the closing prices of Apple and the lower panel with the components of ADX. Along with the components, a grey dashed line is plotted which is the threshold for the ADX plotted at a level of 25. As I said before, the ADX doesn’t track the direction of the trend but instead, the strength and it can be seen several times in the chart where the ADX line increases when the market shows a strong trend (either up or down) and decreases when the market bound to consolidate. This is the same case with both the directional index lines too. We could see that the + DI line increases when the market shows a sturdy uptrend and decreases during a downtrend and vice-versa for the – DI line.<\/p>\n\n\n\n
ADX is not only used to quantify the strength of a market trend but also becomes a handy tool to identify ranging markets (markets where the stock moves back and forth between specific high and low levels showing zero momentum). Whenever the lines move closer to each other, the market is observed to be ranging, similarly, the wider the space between the lines, the more the markets are trending. Those who are introduced to the chart of ADX for the very first time might confuse themselves since the movement of each line is indirectly proportional to the movement of the market.<\/p>\n\n\n\n
Step-6: Creating the trading strategy<\/h3>\n\n\n\n In this step, we are going to implement the discussed Average Directional Index trading strategy in Python.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
def implement_adx_strategy(prices, pdi, ndi, adx):\n buy_price = []\n sell_price = []\n adx_signal = []\n signal = 0\n \n for i in range(len(prices)):\n if adx[i-1] < 25 and adx[i] > 25 and pdi[i] > ndi[i]:\n if signal != 1:\n buy_price.append(prices[i])\n sell_price.append(np.nan)\n signal = 1\n adx_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n adx_signal.append(0)\n elif adx[i-1] < 25 and adx[i] > 25 and ndi[i] > pdi[i]:\n if signal != -1:\n buy_price.append(np.nan)\n sell_price.append(prices[i])\n signal = -1\n adx_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n adx_signal.append(0)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n adx_signal.append(0)\n \n return buy_price, sell_price, adx_signal\n\nbuy_price, sell_price, adx_signal = implement_adx_strategy(aapl['close'], aapl['plus_di'], aapl['minus_di'], aapl['adx'])<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Code Explanation:<\/strong> First, we are defining a function named \u2018implement_adx_strategy\u2019 which takes the stock prices (\u2018prices), and the components of ADX (\u2018pdi\u2019, \u2018ndi\u2019, \u2018adx\u2019) as parameters.<\/p>\n\n\n\nInside the function, we are creating three empty lists (buy_price, sell_price, and adx_signal) in which the values will be appended while creating the trading strategy.<\/p>\n\n\n\n
After that, we are implementing the trading strategy through a for-loop. Inside the for-loop, we are passing certain conditions, and if the conditions are satisfied, the respective values will be appended to the empty lists. If the condition to buy the stock gets satisfied, the buying price will be appended to the \u2018buy_price\u2019 list, and the signal value will be appended as 1 representing to buy the stock. Similarly, if the condition to sell the stock gets satisfied, the selling price will be appended to the \u2018sell_price\u2019 list, and the signal value will be appended as -1 representing to sell the stock.<\/p>\n\n\n\n
Finally, we are returning the lists appended with values. Then, we are calling the created function and stored the values into their respective variables. The list doesn\u2019t make any sense unless we plot the values. So, let\u2019s plot the values of the created trading lists.<\/p>\n\n\n\n
Step-7: Plotting the trading signals<\/h3>\n\n\n\n In this step, we are going to plot the created trading lists to make sense out of them.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
ax1 = plt.subplot2grid((11,1), (0,0), rowspan = 5, colspan = 1)\nax2 = plt.subplot2grid((11,1), (6,0), rowspan = 5, colspan = 1)\nax1.plot(aapl['close'], linewidth = 3, color = '#ff9800', alpha = 0.6)\nax1.set_title('AAPL CLOSING PRICE')\nax1.plot(aapl.index, buy_price, marker = '^', color = '#26a69a', markersize = 14, linewidth = 0, label = 'BUY SIGNAL')\nax1.plot(aapl.index, sell_price, marker = 'v', color = '#f44336', markersize = 14, linewidth = 0, label = 'SELL SIGNAL')\nax2.plot(aapl['plus_di'], color = '#26a69a', label = '+ DI 14', linewidth = 3, alpha = 0.3)\nax2.plot(aapl['minus_di'], color = '#f44336', label = '- DI 14', linewidth = 3, alpha = 0.3)\nax2.plot(aapl['adx'], color = '#2196f3', label = 'ADX 14', linewidth = 3)\nax2.axhline(25, color = 'grey', linewidth = 2, linestyle = '--')\nax2.legend()\nax2.set_title('AAPL ADX 14')\nplt.show()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\nCode Explanation:<\/strong> We are plotting the Average Directional Index components along with the buy and sell signals generated by the trading strategy. We can observe that whenever the ADX line crosses from below to above 25 and the + DI line is above the – DI line, a green-colored buy signal is plotted in the chart. Similarly, whenever the ADX line crosses from below to above 25 and the + DI line is below the – DI line, a red-colored sell signal is plotted in the chart.<\/p>\n\n\n\nStep-8: Creating our Position<\/h3>\n\n\n\n In this step, we are going to create a list that indicates 1 if we hold the stock or 0 if we don\u2019t own or hold the stock.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
position = []\nfor i in range(len(adx_signal)):\n if adx_signal[i] > 1:\n position.append(0)\n else:\n position.append(1)\n \nfor i in range(len(aapl['close'])):\n if adx_signal[i] == 1:\n position[i] = 1\n elif adx_signal[i] == -1:\n position[i] = 0\n else:\n position[i] = position[i-1]\n \nclose_price = aapl['close']\nplus_di = aapl['plus_di']\nminus_di = aapl['minus_di']\nadx = aapl['adx']\nadx_signal = pd.DataFrame(adx_signal).rename(columns = {0:'adx_signal'}).set_index(aapl.index)\nposition = pd.DataFrame(position).rename(columns = {0:'adx_position'}).set_index(aapl.index)\n\nframes = [close_price, plus_di, minus_di, adx, adx_signal, position]\nstrategy = pd.concat(frames, join = 'inner', axis = 1)\n\nstrategy<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\nCode Explanation:<\/strong> First, we are creating an empty list named \u2018position\u2019. We are passing two for-loops, one is to generate values for the \u2018position\u2019 list to just match the length of the \u2018signal\u2019 list. The other for-loop is the one we are using to generate actual position values. Inside the second for-loop, we are iterating over the values of the \u2018signal\u2019 list, and the values of the \u2018position\u2019 list get appended concerning which condition gets satisfied. The value of the position remains 1 if we hold the stock or remains 0 if we sold or don\u2019t own the stock. Finally, we are doing some data manipulations to combine all the created lists into one dataframe.<\/p>\n\n\n\nFrom the output being shown, we can see that in the first row our position in the stock has remained 1 (since there isn\u2019t any change in the ADX signal) but our position suddenly turned to -1 as we sold the stock when the ADX trading signal represents a sell signal (-1). Our position will remain 0 until some changes in the trading signal occur. Now it\u2019s time to do implement some backtesting process!<\/p>\n\n\n\n
Step-9: Backtesting<\/h3>\n\n\n\n Before moving on, it is essential to know what backtesting is. Backtesting is the process of seeing how well our trading strategy has performed on the given stock data. In our case, we are going to implement a backtesting process for our Average Directional Index trading strategy over the Apple stock data.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
aapl_ret = pd.DataFrame(np.diff(aapl['close'])).rename(columns = {0:'returns'})\nadx_strategy_ret = []\n\nfor i in range(len(aapl_ret)):\n returns = aapl_ret['returns'][i]*strategy['adx_position'][i]\n adx_strategy_ret.append(returns)\n \nadx_strategy_ret_df = pd.DataFrame(adx_strategy_ret).rename(columns = {0:'adx_returns'})\ninvestment_value = 100000\nadx_investment_ret = []\n\nfor i in range(len(adx_strategy_ret_df['adx_returns'])):\n number_of_stocks = floor(investment_value\/aapl['close'][i])\n returns = number_of_stocks*adx_strategy_ret_df['adx_returns'][i]\n adx_investment_ret.append(returns)\n\nadx_investment_ret_df = pd.DataFrame(adx_investment_ret).rename(columns = {0:'investment_returns'})\ntotal_investment_ret = round(sum(adx_investment_ret_df['investment_returns']), 2)\nprofit_percentage = floor((total_investment_ret\/investment_value)*100)\nprint(cl('Profit gained from the ADX strategy by investing $100k in AAPL : {}'.format(total_investment_ret), attrs = ['bold']))\nprint(cl('Profit percentage of the ADX strategy : {}%'.format(profit_percentage), attrs = ['bold']))<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\nProfit gained from the ADX strategy by investing $100k in AAPL : 54719.46<\/strong><\/strong>Profit percentage of the ADX strategy : 54%<\/strong><\/code><\/p>\n\n\n\nCode Explanation:<\/strong> First, we are calculating the returns of the Apple stock using the \u2018diff\u2019 function provided by the NumPy package and we have stored it as a dataframe into the \u2018aapl_ret\u2019 variable. Next, we are passing a for-loop to iterate over the values of the \u2018aapl_ret\u2019 variable to calculate the returns we gained from our ADX trading strategy, and these returns values are appended to the \u2018adx_strategy_ret\u2019 list. Next, we are converting the \u2018adx_strategy_ret\u2019 list into a dataframe and stored it into the \u2018adx_strategy_ret_df\u2019 variable.<\/p>\n\n\n\nNext comes the backtesting process. We are going to backtest our strategy by investing a hundred thousand USD into our trading strategy. So first, we are storing the amount of investment into the \u2018investment_value\u2019 variable. After that, we are calculating the number of Apple stocks we can buy using the investment amount. You can notice that I\u2019ve used the \u2018floor\u2019 function provided by the Math package because, while dividing the investment amount by the closing price of Apple stock, it spits out an output with decimal numbers. The number of stocks should be an integer but not a decimal number. Using the \u2018floor\u2019 function, we can cut out the decimals. Remember that the \u2018floor\u2019 function is way more complex than the \u2018round\u2019 function. Then, we are passing a for-loop to find the investment returns followed by some data manipulation tasks.<\/p>\n\n\n\n
Finally, we are printing the total return we got by investing a hundred thousand into our trading strategy and it is revealed that we have made an approximate profit of fifty-four thousand USD in three years. That\u2019s not bad! Now, let\u2019s compare our returns with SPY ETF (an ETF designed to track the S&P 500 stock market index) returns.<\/p>\n\n\n\n
Step-10: SPY ETF Comparison<\/h3>\n\n\n\n This step is optional but it is highly recommended as we can get an idea of how well our trading strategy performs against a benchmark (SPY ETF). In this step, we are going to extract the data of the SPY ETF using the \u2018get_historical_data\u2019 function we created and compare the returns we get from the SPY ETF with our Average Directional Index strategy returns on Apple.<\/p>\n\n\n\n
\n
\n \n
def get_benchmark(start_date, investment_value):\n spy = get_historical_data('SPY', start_date)['close']\n benchmark = pd.DataFrame(np.diff(spy)).rename(columns = {0:'benchmark_returns'})\n \n investment_value = investment_value\n benchmark_investment_ret = []\n \n for i in range(len(benchmark['benchmark_returns'])):\n number_of_stocks = floor(investment_value\/spy[i])\n returns = number_of_stocks*benchmark['benchmark_returns'][i]\n benchmark_investment_ret.append(returns)\n\n benchmark_investment_ret_df = pd.DataFrame(benchmark_investment_ret).rename(columns = {0:'investment_returns'})\n return benchmark_investment_ret_df\n\nbenchmark = get_benchmark('2020-01-01', 100000)\n\ninvestment_value = 100000\ntotal_benchmark_investment_ret = round(sum(benchmark['investment_returns']), 2)\nbenchmark_profit_percentage = floor((total_benchmark_investment_ret\/investment_value)*100)\nprint(cl('Benchmark profit by investing $100k : {}'.format(total_benchmark_investment_ret), attrs = ['bold']))\nprint(cl('Benchmark Profit percentage : {}%'.format(benchmark_profit_percentage), attrs = ['bold']))\nprint(cl('ADX Strategy profit is {}% higher than the Benchmark Profit'.format(profit_percentage - benchmark_profit_percentage), attrs = ['bold']))<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\nBenchmark profit by investing $100k : 37538.96<\/strong><\/strong>Benchmark Profit percentage : 37%<\/strong>ADX Strategy profit is 17% higher than the Benchmark Profit<\/strong><\/code><\/p>\n\n\n\nCode Explanation:<\/strong> The code used in this step is almost similar to the one used in the previous backtesting step but, instead of investing in Apple, we are investing in SPY ETF by not implementing any trading strategies. From the output, we can see that our Average Directional Index trading strategy has outperformed the SPY ETF by 17%. That\u2019s good!<\/p>\n\n\n\nRegister & Get Data<\/span><\/a><\/p>\n\n\n\nFinal Thoughts!<\/h2>\n\n\n\n After a long process of crushing both theory and coding parts, we have successfully learned what the Average Directional Index is all about and how a simple ADX-based trading strategy can be implemented in Python.<\/p>\n\n\n\n
From my perspective, the full power of ADX is unleashed when it is accompanied by another technical indicator, especially with RSI to get quality entry and exit points for your trades. So, it is highly recommended to try improvising this article by tuning the ADX strategy accompanied by other technical indicators and backtesting it as many as possible. Doing this might help in achieving better results in the real-world market. That\u2019s it! Hope you learned something useful from this article.<\/p>\n","protected":false},"excerpt":{"rendered":"
There are four different branches of technical indicators out there but the most popular ones are known as trend indicators. These indicators help traders in identifying the direction and strength of the market trend and trade along with them. In most cases, the indicators that come under the category of trend reveal good results unless […]<\/p>\n","protected":false},"author":18,"featured_media":2471,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[65,42],"tags":[],"coding-language":[30],"ready-to-go-solution":[56],"qualification":[31],"financial-apis-category":[36],"financial-apis-manuals":[39],"class_list":["post-2470","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-backtesting-strategies-examples","category-stocks-data-analysis-examples","coding-language-python","ready-to-go-solution-eodhd-python-financial-library","qualification-experienced","financial-apis-category-stock-market-prices","financial-apis-manuals-end-of-day","has_thumb"],"yoast_head":"\n
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