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\ntsla = extract_historical_data('TSLA', '2020-01-01')\ntsla.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 Tesla. 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: SuperTrend Calculation<\/h2>\n\n\n\n In this step, we are going to calculate the values of the SuperTrend indicator by following the methods we discussed before.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
def get_supertrend(high, low, close, lookback, multiplier):\n \n # ATR\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.ewm(lookback).mean()\n \n # H\/L AVG AND BASIC UPPER & LOWER BAND\n \n hl_avg = (high + low) \/ 2\n upper_band = (hl_avg + multiplier * atr).dropna()\n lower_band = (hl_avg - multiplier * atr).dropna()\n \n # FINAL UPPER BAND final_bands = pd.DataFrame(columns = ['upper', 'lower'])\n final_bands.iloc[:,0] = [x for x in upper_band - upper_band]\n final_bands.iloc[:,1] = final_bands.iloc[:,0] for i in range(len(final_bands)):\n if i == 0:\n final_bands.iloc[i,0] = 0\n else:\n if (upper_band[i] < final_bands.iloc[i-1,0]) | (close[i-1] > final_bands.iloc[i-1,0]):\n final_bands.iloc[i,0] = upper_band[i]\n else:\n final_bands.iloc[i,0] = final_bands.iloc[i-1,0]\n \n # FINAL LOWER BAND\n \n for i in range(len(final_bands)):\n if i == 0:\n final_bands.iloc[i, 1] = 0\n else:\n if (lower_band[i] > final_bands.iloc[i-1,1]) | (close[i-1] < final_bands.iloc[i-1,1]):\n final_bands.iloc[i,1] = lower_band[i]\n else:\n final_bands.iloc[i,1] = final_bands.iloc[i-1,1]\n \n # SUPERTREND\n \n supertrend = pd.DataFrame(columns = [f'supertrend_{lookback}'])\n supertrend.iloc[:,0] = [x for x in final_bands['upper'] - final_bands['upper']]\n \n for i in range(len(supertrend)):\n if i == 0:\n supertrend.iloc[i, 0] = 0\n elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 0] and close[i] < final_bands.iloc[i, 0]:\n supertrend.iloc[i, 0] = final_bands.iloc[i, 0]\n elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 0] and close[i] > final_bands.iloc[i, 0]:\n supertrend.iloc[i, 0] = final_bands.iloc[i, 1]\n elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 1] and close[i] > final_bands.iloc[i, 1]:\n supertrend.iloc[i, 0] = final_bands.iloc[i, 1]\n elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 1] and close[i] < final_bands.iloc[i, 1]:\n supertrend.iloc[i, 0] = final_bands.iloc[i, 0]\n \n supertrend = supertrend.set_index(upper_band.index)\n \n # ST UPTREND\/DOWNTREND\n \n upt = []\n dt = []\n close = close.iloc[len(close) - len(supertrend):]\n\n for i in range(len(supertrend)):\n if close[i] > supertrend.iloc[i, 0]:\n upt.append(supertrend.iloc[i, 0])\n dt.append(np.nan)\n elif close[i] < supertrend.iloc[i, 0]:\n upt.append(np.nan)\n dt.append(supertrend.iloc[i, 0])\n else:\n upt.append(np.nan)\n dt.append(np.nan)\n \n st, upt, dt = pd.Series(supertrend.iloc[:, 0]), pd.Series(upt), pd.Series(dt)\n upt.index, dt.index = supertrend.index, supertrend.index\n \n return st, upt, dt\n\ntsla['st'], tsla['s_upt'], tsla['st_dt'] = get_supertrend(tsla['high'], tsla['low'], tsla['close'], 10, 3)\ntsla = tsla[1:]\ntsla.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_supertrend\u2019 which takes a stock\u2019s high (\u2018high\u2019), low (\u2018low\u2019), close (\u2018close\u2019), the lookback period (\u2018lookback), and the multiplier (\u2018multiplier\u2019) as parameters. The code inside the function can be divided into six parts: ATR calculation, HLA and basic bands calculation, final upper band calculation, final lower band calculation, SuperTrend indicator calculation, and determining the uptrend and downtrend of the indicator.<\/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 function, we are taking the Exponential Moving Average of True Range for a specified number of periods to get the ATR values. Many prefer using SMA for ATR calculation while determining the SuperTrend, but I used EMA for more accuracy.<\/p>\n\n\n\n
HLA and Basic Bands calculation: Before calculating the basic upper and lower bands, we need to first determine the High Low Average. To calculate the values of HLA, we are first finding the total of the high and low values of a stock, then dividing the total by 2. Using the HLA values which are stored into the \u2018hl_avg\u2019 variable, we are determining both the upper and lower bands by following the formula we discussed and stored the values into the \u2018upper_band\u2019 and \u2018lower_band\u2019 respectively.<\/p>\n\n\n\n
Final Upper Band calculation: Before directly moving into calculating the final upper band values, we are first creating a dataframe named \u2018final_bands\u2019 to store both the final upper and lower bands. Then just for the sake to fill the dataframe with values, we are subtracting the upper band by itself to store zeros matching the length of the upper band series so that it can be used for iterations. This step of creating the dataframe is optional but highly recommended since it reduces future works of data processing and all similar stuff. After creating the dataframe, we are passing a for-loop to create the conditions we discussed before for determining the values of the final upper band.<\/p>\n\n\n\n
Final Lower Band calculation: The code structure for determining the final lower band is most similar to the final upper band calculation but only the conditions change. But now, let\u2019s dive into exploring what\u2019s happening inside the for-loop we are passing to get the values of the final lower band. First, we are passing a for-loop that iterates over the length of the \u2018final_bands\u2019 dataframe we created before. Inside the for-loop, we are first defining an if-statement that appends the final lower band\u2019s value as 0 if the current iteration value is zero. This if-statement\u2019s purpose is to fill the first value of the final lower band as zero. After the if-statement, we are defining nested else-statement which appends the final lower band\u2019s value as the basic lower band value if the condition we discussed before gets satisfied, or else, it appends the previous final lower band value.<\/p>\n\n\n\n
SuperTrend calculation: We are first creating a dataframe named \u2018supertrend\u2019 to store the values of the SuperTrend indicator. Like how we did before just to fill the dataframe with zeros to match the length of the basic bands series, we are doing the same here too to match the final bands series. Then comes the for-loop to determine the values of the SuperTrend indicator which appends the values concerning which condition gets satisfied out of the four. After the for-loop, we are doing some data processing to modify a bit and clean the dataframe.<\/p>\n\n\n\n
Uptrend\/Downtrend determination: This step is optional as I did only to make the visualization of the indicator a bit easy but you can try doing this too. The main aim of this step is to classify the SuperTrend indicator\u2019s periods into two categories: the SuperTrend being below the closing price (uptrend), the SuperTrend being above the closing price (downtrend). With that being said, let\u2019s dive into the methods involved. We are first defining two empty lists named \u2018up\u2019 and \u2018down\u2019 in which the values of the uptrend and the downtrend will be appended respectively. We are also reducing the length of the closing price data to match that of the SuperTrend data, only then, the iteration would be possible. Next, we are passing a for-loop to iterate over the length of the SuperTrend data to determine and append both uptrend and downtrend values into their respective variables. The values will be appended concerning which condition gets satisfied that are defined inside the for-loop. If either condition gets satisfied, the values of both uptrend and downtrend will be appended as \u2018NaN\u2019 (not defined). Then, we are converting the lists we created to store the uptrend and downtrend into Pandas series as it will be more convenient to work with.<\/p>\n\n\n\n
After calculating all these values, we are finally returning the SuperTrend indicator values, and both the uptrend and downtrend readings. Then, we are calling the created function to store the SuperTrend indicator values of Tesla along with the uptrend and downtrend readings with 10 as the lookback period and 3 as the multiplier. From the output being shown, it is observable that whenever the closing price is greater than the SuperTrend indicator, the downtrend readings (\u2018st_dt\u2019) represent \u2018NaN\u2019. Likewise, whenever the closing price is lesser than the SuperTrend indicator, the uptrend readings (\u2018st_upt\u2019) represent \u2018NaN\u2019.<\/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 SuperTrend indicator crossover trading strategy in python.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
def implement_st_strategy(prices, st):\n buy_price = []\n sell_price = []\n st_signal = []\n signal = 0\n \n for i in range(len(st)):\n if st[i-1] > prices[i-1] and st[i] < prices[i]:\n if signal != 1:\n buy_price.append(prices[i])\n sell_price.append(np.nan)\n signal = 1\n st_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n st_signal.append(0)\n elif st[i-1] < prices[i-1] and st[i] > prices[i]:\n if signal != -1:\n buy_price.append(np.nan)\n sell_price.append(prices[i])\n signal = -1\n st_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n st_signal.append(0)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n st_signal.append(0)\n \n return buy_price, sell_price, st_signal\n\nbuy_price, sell_price, st_signal = implement_st_strategy(tsla['close'], tsla['st'])<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Code Explanation: First, we are defining a function named \u2018implement_st_strategy\u2019 which takes the stock prices (\u2018prices\u2019), and the values of the SuperTrend indicator (\u2018st\u2019) as parameters.<\/p>\n\n\n\n
Inside the function, we are creating three empty lists (buy_price, sell_price, and st_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
plt.plot(tsla['close'], linewidth = 2)\n
\n \n <\/use>\n <\/svg>\n Output:<\/strong><\/p>\n\n\n\nCode Explanation:<\/strong> We are plotting the readings of the SuperTrend indicator along with the buy and sell signals generated by the trading strategy. We can observe that whenever the SuperTrend indicator line crosses from above to below the closing price line, a green-colored buy signal is plotted in the chart. Similarly, whenever the SuperTrend indicator line crosses from below to above the closing price line, a red-colored sell signal is plotted in the chart.<\/p>\n\n\n\nStep-6: 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(st_signal)):\n if st_signal[i] > 1:\n position.append(0)\n else:\n position.append(1)\n \nfor i in range(len(tsla['close'])):\n if st_signal[i] == 1:\n position[i] = 1\n elif st_signal[i] == -1:\n position[i] = 0\n else:\n position[i] = position[i-1]\n \nclose_price = tsla['close']\nst = tsla['st']\nst_signal = pd.DataFrame(st_signal).rename(columns = {0:'st_signal'}).set_index(tsla.index)\nposition = pd.DataFrame(position).rename(columns = {0:'st_position'}).set_index(tsla.index)\n\nframes = [close_price, st, st_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 two rows our position in the stock has remained 1 (since there isn\u2019t any change in the SuperTrend indicator signal) but our position suddenly turned to -1 as we sold the stock when the SuperTrend 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-7: 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 SuperTrend indicator trading strategy over the Tesla stock data.<\/p>\n\n\n\n
Python Implementation:<\/strong><\/p>\n\n\n\n \n
\n \n
tsla_ret = pd.DataFrame(np.diff(tsla['close'])).rename(columns = {0:'returns'})\nst_strategy_ret = []\n\nfor i in range(len(tsla_ret)):\n returns = tsla_ret['returns'][i]*strategy['st_position'][i]\n st_strategy_ret.append(returns)\n \nst_strategy_ret_df = pd.DataFrame(st_strategy_ret).rename(columns = {0:'st_returns'})\ninvestment_value = 100000\nst_investment_ret = []\n\nfor i in range(len(st_strategy_ret_df['st_returns'])):\n number_of_stocks = floor(investment_value\/tsla['close'][i])\n returns = number_of_stocks*st_strategy_ret_df['st_returns'][i]\n st_investment_ret.append(returns)\n\nst_investment_ret_df = pd.DataFrame(st_investment_ret).rename(columns = {0:'investment_returns'})\ntotal_investment_ret = round(sum(st_investment_ret_df['investment_returns']), 2)\nprofit_percentage = floor((total_investment_ret\/investment_value)*100)\nprint(cl('Profit gained from the ST strategy by investing $100k in TSLA : {}'.format(total_investment_ret), attrs = ['bold']))\nprint(cl('Profit percentage of the ST 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 ST strategy by investing $100k in TSLA : 232677.17\nProfit percentage of the ST strategy : 232%<\/strong><\/pre>\n\n\n\nCode Explanation:<\/strong> First, we are calculating the returns of the Tesla stock using the \u2018diff\u2019 function provided by the NumPy package and we have stored it as a dataframe into the \u2018tsla_ret\u2019 variable. Next, we are passing a for-loop to iterate over the values of the \u2018tsla_ret\u2019 variable to calculate the returns we gained from our SuperTrend indicator trading strategy, and these returns values are appended to the \u2018st_strategy_ret\u2019 list. Next, we are converting the \u2018st_strategy_ret\u2019 list into a dataframe and stored it into the \u2018st_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 Tesla 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 Tesla 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 two hundred and thirty-two thousand USD in one year. That\u2019s wonderful! 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-8: 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 SuperTrend indicator trading strategy returns on Tesla.<\/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)\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('ST 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%\nST Strategy profit is 191% 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 Tesla, we are investing in SPY ETF by not implementing any trading strategies. From the output, we can see that our SuperTrend indicator trading strategy has outperformed the SPY ETF by 191%. 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 SuperTrend indicator is all about, its calculation, and how to build a simple crossover trading strategy based on it in python. One important thing we managed to accomplish in this article is understanding the complex math behind the indicator and this will help in increasing your understanding of the indicator significantly.<\/p>\n\n\n\n
As the SuperTrend indicator has just started gaining momentum, there are a lot more spaces for improvement. One such important space is strategy optimization. I talk about this in almost every article of mine as it should be considered of paramount importance.<\/p>\n\n\n\n
For those who don\u2019t know what strategy optimization is, it is the process of tuning the trading algorithm to perform at its best. You can tune the SuperTrend indicator by experimenting with different settings. In this article, we built the indicator with the traditional setting of 14 as the lookback period and 3 as the multiplier but, you can try changing the values and run backtests for each and every change to acknowledge its performance. By doing this will help you reach the optimal setting for the indicator that can outperform the market itself.<\/p>\n\n\n\n
We didn’t consider doing it in this article, as the sole purpose is not on building an optimistic trading strategy ,but on building a strong intuition on what the SuperTrend indicator is all about. But, it is highly recommended to get your hands dirty on experimenting with the indicator and introduce yourselves to a whole new level of possibilities. With that being said, you\u2019ve reached the end of the article. Hope you learned something useful from this article. Happy programming!<\/p>\n","protected":false},"excerpt":{"rendered":"
I\u2019ve backtested numerous trading strategies using a wide variety of technical indicators. Among these, a few indicators have delivered exceptional results, and I consider them to be “premium” indicators. In today’s analysis, we\u2019ll focus on one such trend-following indicator that has earned its place in this exclusive group due to its accuracy and efficiency\u2014the SuperTrend indicator. […]<\/p>\n","protected":false},"author":18,"featured_media":2141,"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-2140","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
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