Register & Get Data<\/span><\/a><\/p>\n\n\n\nStep-3: Extracting Historical Data<\/h2>\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 extract_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\nnflx = get_historical_data('NFLX', '2020-01-01')\nnflx.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 eod<\/code> package to extract the split-adjusted historical stock data of Netflix. 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-3: Williams %R Calculation<\/h2>\n\n\n\n In this step, we are going to calculate the values of Williams %R by following the formula we discussed before.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
def get_wr(high, low, close, lookback):\n highh = high.rolling(lookback).max() \n lowl = low.rolling(lookback).min()\n wr = -100 * ((highh - close) \/ (highh - lowl))\n return wr\n\nnflx['wr_14'] = get_wr(nflx['high'], nflx['low'], nflx['close'], 14)\nnflx = nflx.dropna()\nnflx.head()<\/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_wr\u2019 that takes a stock\u2019s high price data (\u2018high\u2019), low price data (\u2018low\u2019), closing price data (\u2018close\u2019), and the lookback period (\u2018period\u2019) as parameters. Inside the function, we are first determining the highest high over a specific lookback period timeframe with the help of the \u2018rolling\u2019 and \u2018max\u2019 functions provided by the Pandas package and stored it into the \u2018highh\u2019 variable. What the \u2018rolling\u2019 function performs is that it will take into account the n-period timeframe we specify and the \u2018max\u2019 function filters the maximum values present in the given dataframe.<\/p>\n\n\n\nNext, we are defining a variable named \u2018lowl\u2019 to store the lowest low over a specified lookback period timeframe which we determined using the \u2018rolling\u2019 and \u2018min\u2019 function (as the name suggests, filters the minimum values in the given dataframe) provided by the Pandas package.<\/p>\n\n\n\n
Then, we are substituting the determined highest high and lowest low values into the formula we discussed before to calculate the values of Williams %R and stored it into the \u2018wr\u2019 variable. Finally, we are returning and calling the created function to store Netflix\u2019s Williams %R readings with 14 as the lookback period.<\/p>\n\n\n\n
Step-4: Williams %R Plot<\/h2>\n\n\n\n In this step, we are going to plot the calculated Williams %R values of Netflix to make more sense of them. 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 Williams %R technical indicator.<\/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)\n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\n<\/p>\n\n\n\n
The above chart is divided into two panels: The upper panel with the closing price of Netflix\u2019s stock data and the lower panel with the values of Netflix\u2019s 14-day readings of Williams %R. Now, the chart can be utilized in two ways. The first way is using the chart as a tool to identify overbought and oversold states of the market. You could observe that there are two horizontal grey lines plotted above and below the market which are the overbought and oversold levels plotted at a threshold of -20 and -80 respectively. You can consider that the market is in the state of overbought if the Williams %R has a reading of above the upper line or the overbought line. Similarly, you can assume that the market is in the state of oversold if the Williams %R has a reading of below the lower line or the oversold line.<\/p>\n\n\n\n
The second way of using Williams %R is to identify false momentum in the market. During a sturdy uptrend, the readings of Williams %R tend to reach above -20 frequently. If the indicator falls and struggles to reach above -20 before the next fall, indicates that the market\u2019s momentum is not authentic and possible to follow a tremendous downtrend. Likewise, during a healthy downtrend, the readings of Williams %R bound to go below -80 frequently. If the indicator rises and fails to reach -80 before the next rise, reveals that the market is going to follow a positive trend.<\/p>\n\n\n\n
Since Williams %R is a directional indicator (an indicator whose movement is directly proportional to that of the actual market), traders also use this indicator to find and confirm strong uptrends or downtrends in a market and trade along with it. Some indicators are not of much use while utilized for identifying or confirming market trends as they might be lagging in nature (an indicator that takes into account the historical data points to determine the current reading) but Williams %R is an effective one because it is a leading indicator (an indicator that takes into account the previous data points to predict the future movements).<\/p>\n\n\n\n
Step-5: Creating the trading strategy<\/h2>\n\n\n\n In this step, we are going to implement the discussed Williams %R trading strategy in Python.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
def implement_wr_strategy(prices, wr): \n buy_price = []\n sell_price = []\n wr_signal = []\n signal = 0\n\n for i in range(len(wr)):\n if wr[i-1] > -80 and wr[i] < -80:\n if signal != 1:\n buy_price.append(prices[i])\n sell_price.append(np.nan)\n signal = 1\n wr_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n wr_signal.append(0)\n elif wr[i-1] < -20 and wr[i] > -20:\n if signal != -1:\n buy_price.append(np.nan)\n sell_price.append(prices[i])\n signal = -1\n wr_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n wr_signal.append(0)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n wr_signal.append(0)\n \n return buy_price, sell_price, wr_signal\n \nbuy_price, sell_price, wr_signal = implement_wr_strategy(nflx['close'], nflx['wr_14'])<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Code Explanation:<\/strong> First, we are defining a function named \u2018implement_wr_strategy\u2019 which takes the stock prices (\u2018prices\u2019), and the values of Williams %R indicator (\u2018wr\u2019) as parameters.<\/p>\n\n\n\nInside the function, we are creating three empty lists (buy_price, sell_price, and wr_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-6: Plotting the trading signals<\/h2>\n\n\n\n In this step, we are going to plot the created trading lists to make sense 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(nflx['close'], linewidth = 2)\nax1.plot(nflx.index, buy_price, marker = '^', markersize = 12, linewidth = 0, color = 'green', label = 'BUY SIGNAL')\nax1.plot(nflx.index, sell_price, marker = 'v', markersize = 12, linewidth = 0, color = 'r', label = 'SELL SIGNAL')\nax1.legend()\nax1.set_title('NFLX TRADING SIGNALS')\nax2.plot(nflx['wr_14'], color = 'orange', linewidth = 2)\nax2.axhline(-20, linewidth = 1.5, linestyle = '--', color = 'grey')\nax2.axhline(-80, linewidth = 1.5, linestyle = '--', color = 'grey')\nax2.set_title('NFLX WILLIAMS %R 14')\nplt.show()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\n<\/p>\n\n\n\n
Code Explanation:<\/strong> We are plotting the readings of Williams %R along with the buy and sell signals generated by the trading strategy. We can observe that whenever the Williams %R line crosses from below to above -20 and, a green-colored buy signal is plotted in the chart. Similarly, whenever the Williams %R line crosses from above to below -80 and, a red-colored sell signal is plotted in the chart.<\/p>\n\n\n\nStep-7: Creating our Position<\/h2>\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(wr_signal)):\n if wr_signal[i] > 1:\n position.append(0)\n else:\n position.append(1)\n \nfor i in range(len(nflx['close'])):\n if wr_signal[i] == 1:\n position[i] = 1\n elif wr_signal[i] == -1:\n position[i] = 0\n else:\n position[i] = position[i-1]\n \nclose_price = nflx['close']\nwr = nflx['wr_14']\nwr_signal = pd.DataFrame(wr_signal).rename(columns = {0:'wr_signal'}).set_index(nflx.index)\nposition = pd.DataFrame(position).rename(columns = {0:'wr_position'}).set_index(nflx.index)\n\nframes = [close_price, wr, wr_signal, position]\nstrategy = pd.concat(frames, join = 'inner', axis = 1)\n\nstrategy.head()<\/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 Williams %R signal) but our position suddenly turned to -1 as we sold the stock when the Williams %R 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-8: Backtesting<\/h2>\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 Williams %R trading strategy over the Netflix stock data.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
nflx_ret = pd.DataFrame(np.diff(nflx['close'])).rename(columns = {0:'returns'})\nwr_strategy_ret = []\n\nfor i in range(len(nflx_ret)):\n returns = nflx_ret['returns'][i]*strategy['wr_position'][i]\n wr_strategy_ret.append(returns)\n \nwr_strategy_ret_df = pd.DataFrame(wr_strategy_ret).rename(columns = {0:'wr_returns'})\ninvestment_value = 100000\nwr_investment_ret = []\n\nfor i in range(len(wr_strategy_ret_df['wr_returns'])):\n number_of_stocks = floor(investment_value\/nflx['close'][i])\n returns = number_of_stocks*wr_strategy_ret_df['wr_returns'][i]\n wr_investment_ret.append(returns)\n\nwr_investment_ret_df = pd.DataFrame(wr_investment_ret).rename(columns = {0:'investment_returns'})\ntotal_investment_ret = round(sum(wr_investment_ret_df['investment_returns']), 2)\nprofit_percentage = floor((total_investment_ret\/investment_value)*100)\nprint(cl('Profit gained from the W%R strategy by investing $100k in NFLX : {}'.format(total_investment_ret), attrs = ['bold']))\nprint(cl('Profit percentage of the W%R 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 W%R strategy by investing $100k in NFLX : 57772.45<\/strong>Profit percentage of the W%R strategy : 57%<\/strong><\/pre>\n\n\n\nCode Explanation:<\/strong> First, we are calculating the returns of the Netflix stock using the \u2018diff\u2019 function provided by the NumPy package and we have stored it as a dataframe into the \u2018nflx_ret\u2019 variable. Next, we are passing a for-loop to iterate over the values of the \u2018nflx_ret\u2019 variable to calculate the returns we gained from our Williams %R trading strategy, and these returns values are appended to the \u2018wr_strategy_ret\u2019 list. Next, we are converting the \u2018wr_strategy_ret\u2019 list into a dataframe and stored it into the \u2018wr_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 Netflix 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 Netflix 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-seven thousand USD in one year. 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-9: SPY ETF Comparison<\/h2>\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 Williams %R trading strategy returns on Netflix.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/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('W%R 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 : 22431.5<\/strong>Benchmark Profit percentage : 22%<\/strong>W%R Strategy profit is 35% higher than the Benchmark Profit<\/strong><\/pre>\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 Netflix, we are investing in SPY ETF by not implementing any trading strategies. From the output, we can see that our Williams %R trading strategy has outperformed the SPY ETF by 35%. That\u2019s great!<\/p>\n\n\n\nRegister & Get Data<\/span><\/a><\/p>\n\n\n\nFinal Thoughts!<\/h1>\n\n\n\n After a long process of crushing both theory and coding parts on Williams %R, we have successfully built a profitable trading strategy that exceeds the returns of SPY ETF. That\u2019s great but, that isn’t enough. When I ran a separate backtest process to gain more insights on the performance of our trading strategy, I came to know that our Williams %R-based strategy returns are less than that of the actual Netflix stock return. The foremost reason behind this might be strategy optimization.<\/p>\n\n\n\n
What is strategy optimization? It is the process of tuning a trading strategy to perform to its best. The best way to tune a strategy, especially a leading indicator trading strategy is to add another technical indicator as a filter. This filter acts as a gauge to ensure that the trading signals being revealed by the strategy are authentic but not false. This part should be considered essential while using Williams %R since this indicator is prone to revealing many false signals which ultimately ends up resulting in us making bad trades. Coming back to strategy optimization, it is not only about adding another technical indicator but also includes efficient risk management steps, and a better trading environment.<\/p>\n\n\n\n
If you manage to achieve these things, you will have a robust trading algorithm in your hand that is ready to make some better trades in the real-world market. So I highly recommend you to try these things out. You might question me on why I haven’t touched upon these topics in the article because the sole incentive of the article is not to encourage people to create profitable trading strategies and mint money from the market but to educate people about a powerful trading indicator. That\u2019s it! You\u2019ve reached the end of the article. Hope you learned something new and useful from this article.<\/p>\n","protected":false},"excerpt":{"rendered":"
While having a look at the list of most popular momentum indicators that consists of the Relative Strength Index, and the Stochastic Oscillator, the one we are going to discuss today also joins the list when considering its usage and efficiency in the real world market. It\u2019s an indicator known as Williams %R. In this […]<\/p>\n","protected":false},"author":18,"featured_media":2151,"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":[65,59,61],"tags":[],"coding-language":[30],"ready-to-go-solution":[56],"qualification":[31,32],"financial-apis-category":[36],"financial-apis-manuals":[39,38,40],"class_list":["post-2150","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-backtesting-strategies-examples","category-technical-analysis-examples","category-trading-indicators-implimentations","coding-language-python","ready-to-go-solution-eodhd-python-financial-library","qualification-experienced","qualification-guru","financial-apis-category-stock-market-prices","financial-apis-manuals-end-of-day","financial-apis-manuals-intraday","financial-apis-manuals-technical-indicators","has_thumb"],"yoast_head":"\n
Algorithmic Trading with Williams %R in Python: A Comprehensive Guide | EODHD APIs Academy<\/title>\n<meta name=\"description\" content=\"Learn about algorithmic trading with Williams %R indicator using Python. Explore the power of coding and automate your trading strategies.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eodhd.com\/financial-academy\/backtesting-strategies-examples\/algorithmic-trading-with-williams-r-in-python\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Algorithmic Trading with Williams %R in\u00a0Python\" \/>\n<meta property=\"og:description\" content=\"Learn about algorithmic trading with Williams %R indicator using Python. 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