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\nintc = get_historical_data('INTC', '2020-01-01')\nintc.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 Intel. 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: Keltner Channel Calculation<\/h2>\n\n\n\n In this step, we are going to calculate the components of the Keltner Channel indicator by following the methods we discussed before.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
# KELTNER CHANNEL CALCULATION\n\ndef get_kc(high, low, close, kc_lookback, multiplier, atr_lookback):\n tr1 = pd.DataFrame(high - low)\n tr2 = pd.DataFrame(abs(high - close.shift()))\n tr3 = pd.DataFrame(abs(low - close.shift()))\n frames = [tr1, tr2, tr3]\n tr = pd.concat(frames, axis = 1, join = 'inner').max(axis = 1)\n atr = tr.ewm(alpha = 1\/atr_lookback).mean()\n \n kc_middle = close.ewm(kc_lookback).mean()\n kc_upper = close.ewm(kc_lookback).mean() + multiplier * atr\n kc_lower = close.ewm(kc_lookback).mean() - multiplier * atr\n \n return kc_middle, kc_upper, kc_lower\n \nintc = intc.iloc[:,:4]\nintc['kc_middle'], intc['kc_upper'], intc['kc_lower'] = get_kc(intc['high'], intc['low'], intc['close'], 20, 2, 10)\nintc.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_kc\u2019 that takes a stock\u2019s high (\u2018high\u2019), low (\u2018low\u2019), and closing price data (\u2018close\u2019), the lookback period for the Keltner Channel (\u2018kc_lookback\u2019), the multiplier value (\u2018multiplier), and the lookback period for the ATR (\u2018atr_lookback\u2019) as parameters. The code inside the function can be separated into two parts: ATR calculation, and the Keltner Channel calculation.<\/p>\n\n\n\nATR calculation: To determine the readings of the Average True Range, we are first calculating the three differences and stored them into their respective variables. Then we are combining all three differences into one dataframe using the \u2018concat\u2019 function and took the maximum values out of the three collective differences to determine the True Range. Then, using the \u2018ewm\u2019 and \u2018mean\u2019 functions, we are taking the customized Moving Average of True Range for a specified number of periods to get the ATR values.<\/p>\n\n\n\n
Keltner Channel calculation: Utilizing the previously calculated ATR values, we are first calculating the middle line of the Keltner Channel by taking the EMA of ATR for a specified number of periods. Then comes the calculation of both the upper and lower bands. We are substituting the ATR values into the upper and lower bands formula we discussed before to get the readings of each of them. Finally, we are returning and calling the created function to get the Keltner Channel values of Intel.<\/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 Keltner Channel indicator breakout trading strategy in python.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
# KELTNER CHANNEL STRATEGY\n\ndef implement_kc_strategy(prices, kc_upper, kc_lower):\n buy_price = []\n sell_price = []\n kc_signal = []\n signal = 0\n \n for i in range(len(prices)):\n if prices[i] < kc_lower[i] and prices[i+1] > prices[i]:\n if signal != 1:\n buy_price.append(prices[i])\n sell_price.append(np.nan)\n signal = 1\n kc_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n kc_signal.append(0)\n elif prices[i] > kc_upper[i] and prices[i+1] < prices[i]:\n if signal != -1:\n buy_price.append(np.nan)\n sell_price.append(prices[i])\n signal = -1\n kc_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n kc_signal.append(0)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n kc_signal.append(0)\n \n return buy_price, sell_price, kc_signal\n\nbuy_price, sell_price, kc_signal = implement_kc_strategy(intc['close'], intc['kc_upper'], intc['kc_lower'])<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Code Explanation:<\/strong> First, we are defining a function named \u2018implement_kc_strategy\u2019 which takes the stock prices (\u2018prices\u2019), and the components of the Keltner Channel indicator (\u2018kc_upper\u2019, and \u2018kc_lower\u2019) as parameters.<\/p>\n\n\n\nInside the function, we are creating three empty lists (buy_price, sell_price, and kc_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 out of them.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
# TRADING SIGNALS PLOT\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 the components of the Keltner Channel indicator along with the buy and sell signals generated by the breakout trading strategy. We can observe that whenever the closing price line from above to below the lower band line and the current closing price is lower than the next closing price, a green-colored buy signal is plotted in the chart. Similarly, whenever the closing price line crosses from below to above the upper band and the current closing price is greater than the next closing price, 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
# STOCK POSITION\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 Keltner Channel indicator signal) but our position suddenly turned to -1 as we sold the stock when the Keltner Channel indicator 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 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 Keltner Channel indicator trading strategy over the Intel stock data.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
# BACKTESTING\n<\/strong>\nintc_ret = pd.DataFrame(np.diff(intc['close'])).rename(columns = {0:'returns'})\nkc_strategy_ret = []\n\nfor i in range(len(intc_ret)):\n returns = intc_ret['returns'][i]*strategy['kc_position'][i]\n kc_strategy_ret.append(returns)\n \nkc_strategy_ret_df = pd.DataFrame(kc_strategy_ret).rename(columns = {0:'kc_returns'})\ninvestment_value = 100000\nkc_investment_ret = []\n\nfor i in range(len(kc_strategy_ret_df['kc_returns'])):\n number_of_stocks = floor(investment_value\/intc['close'][i])\n returns = number_of_stocks*kc_strategy_ret_df['kc_returns'][i]\n kc_investment_ret.append(returns)\n\nkc_investment_ret_df = pd.DataFrame(kc_investment_ret).rename(columns = {0:'investment_returns'})\ntotal_investment_ret = round(sum(kc_investment_ret_df['investment_returns']), 2)\nprofit_percentage = floor((total_investment_ret\/investment_value)*100)\nprint(cl('Profit gained from the KC strategy by investing $100k in INTC : {}'.format(total_investment_ret), attrs = ['bold']))\nprint(cl('Profit percentage of the KC 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 KC strategy by investing $100k in INTC : 82463.64\nProfit percentage of the KC strategy : 82%<\/strong><\/pre>\n\n\n\nCode Explanation:<\/strong> First, we are calculating the returns of the Intel stock using the \u2018diff\u2019 function provided by the NumPy package and we have stored it as a dataframe into the \u2018intc_ret\u2019 variable. Next, we are passing a for-loop to iterate over the values of the \u2018intc_ret\u2019 variable to calculate the returns we gained from our Keltner Channel indicator trading strategy, and these returns values are appended to the \u2018kc_strategy_ret\u2019 list. Next, we are converting the \u2018kc_strategy_ret\u2019 list into a dataframe and stored it into the \u2018kc_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 Intel 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 Intel 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 eighty-two thousand USD in one year. That\u2019s not bad at all! 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 Keltner Channel breakout trading strategy returns on Intel.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
# SPY ETF COMPARISON\n<\/strong>\ndef 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)\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('KC 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 : 41144.52\nBenchmark Profit percentage : 41%\nKC Strategy profit is 41% 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 Intel, we are investing in SPY ETF by not implementing any trading strategies. From the output, we can see that our Keltner Channel breakout trading strategy has outperformed the SPY ETF by 41%. 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, we have successfully learned what the Keltner Channel indicator is all about, the math behind the indicator, and finally, how to build the indicator from scratch and construct the breakout trading strategy in Python. We also did manage to get some nice results and in fact, apart from surpassing the returns of the SPY ETF, we exceeded the actual Intel stock returns itself with our breakout strategy.<\/p>\n\n\n\n
I often talk about strategy tuning or optimization in my articles and today, we really did implement it by tuning and making some changes to the traditional breakout strategy. As a result, we were able to outdo the returns of the actual market itself. This is just one small example of how to tune a strategy and how the results will be impacted accordingly but, there is a lot more to be explored. Strategy optimization is not only about tuning or making some changes to the traditional strategies that have existed for a long time but also about creating an optimal trading environment and this includes the broker you are using for trading purposes, the risk management system, and so on. So it\u2019s highly recommended to have a look at these spaces to take your strategies to a whole new level.<\/p>\n\n\n\n
With that said, you have reached the end of the article. Hope you learned something new and useful from this article.<\/p>\n","protected":false},"excerpt":{"rendered":"
Studying technical indicators, you will definitely come across a list comprising curated indicators that are widely considered as \u2018must-know\u2019 indicators that need to be learned before getting your hands dirty in the real-world market. The indicator we are going to explore today adds to this list, given its performance in the market: the Keltner Channel […]<\/p>\n","protected":false},"author":18,"featured_media":2117,"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],"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-2116","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-backtesting-strategies-examples","category-technical-analysis-examples","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
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