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df = api.get_exchanges()\n print(df[df[\"Name\"].str.contains(\"London Exchange\")])\n<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n I can see it’s “LSE<\/strong>“. I know the index I’m looking for is an ETF from HSBC.<\/p>\n\n\n\n \n
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df = api.get_exchange_symbols(\"LSE\")\n df = df[df[\"Type\"] == \"ETF\"]\n df = df[df[\"Name\"].str.contains(\"HSBC\")]\n df = df[df[\"Name\"].str.contains(\"500\")]\n print(df)<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n I can see it there, it’s “HSPX<\/strong>“.<\/p>\n\n\n\nI now want to populate three data frames with Weekly, Daily and Hourly data.<\/p>\n\n\n\n
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df_weekly = api.get_historical_data(symbol=\"HSPX.LSE\", interval=\"w\", iso8601_start=\"2018-01-01\", iso8601_end=\"2023-09-04\")\n print(df_weekly)<\/code><\/pre>\n\n <\/div>\n \n
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df_daily = api.get_historical_data(symbol=\"HSPX.LSE\", interval=\"d\", iso8601_start=\"2022-01-01\", iso8601_end=\"2023-09-04\")\n print(df_daily)\n<\/code><\/pre>\n\n <\/div>\n \n
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df_hourly = api.get_historical_data(symbol=\"HSPX.LSE\", interval=\"1h\", iso8601_start=\"2023-01-01\", iso8601_end=\"2023-09-04\")\n print(df_hourly)<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Calculating the 200-interval Simple Moving Averages (SMA)<\/h2>\n\n\n\n It’s time to introduce the trading strategy…<\/p>\n\n\n\n
What we want to do is calculate the SMA200 for the weekly, daily, and hourly. The first deciding factor is if the SMA200 for the daily is above or below the SMA200 for the weekly. If it’s above, then the long term trend is up and we want to be trading, and if it’s below the long term trend is down and we don’t want to be trading.<\/p>\n\n\n\n
Assuming the SMA200 for the daily is above the SMA200 for the weekly, we then want to compare the SMA200 for the daily with the SMA200 for the hourly. If the SMA200 for the hourly crosses above the SMA200 for the daily, that’s a buy signal. If the SMA200 for the hourly crosses below the SMA200 for the daily, that’s a sell signal.<\/p>\n\n\n\n
Now that we’ve covered the basics, let’s create our moving averages for the three datasets.<\/p>\n\n\n\n
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df_weekly[\"Weekly SMA200\"] = df_weekly[\"adjusted_close\"].rolling(200, min_periods=200).mean()\n<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n As you can see we now have a feature called “Weekly SMA200” which starts showing the mean of the previous 200 weeks. The NaN means “Not a Number” and just means that the first 200 weeks we don’t have a mean yet.<\/p>\n\n\n\n
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df_daily[\"Daily SMA200\"] = df_daily[\"adjusted_close\"].rolling(200, min_periods=200).mean()\n\ndf_hourly[\"Hourly SMA200\"] = df_hourly[\"close\"].rolling(200, min_periods=200).mean()\n<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Merging the Daily and Weekly datasets<\/h2>\n\n\n\n We will now want to start merging the three datasets for comparison. A copy of the daily dataset with the SMA200 will be created and the SMA200 for the weekly will be merged in.<\/p>\n\n\n\n
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df_weekly_daily = df_daily.copy()\n df_weekly_daily[\"Weekly SMA200\"] = df_weekly[\"Weekly SMA200\"]\n print(df_weekly_daily)\n<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n To visualise this we can use the “plot” function in Pandas, which uses Matplotlib.<\/p>\n\n\n\n
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import matplotlib.pyplot as plt\n\n df_plot = df_weekly_daily.dropna().loc[\"2022\":\"2023\", [\"adjusted_close\", \"Daily SMA200\", \"Weekly SMA200\"]]\n df_plot.rename(columns={\"adjusted_close\": \"Close\"}, inplace=True)\n print(df_plot)\n df_plot.plot(title=\"S&P 500 Weekly SMA 200 vs. Daily SMA 200\", kind=\"line\", color=[\"black\", \"green\", \"blue\"], figsize=(36, 12))\n plt.show()\n\n<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n The blue line is the weekly SMA200, and as you can see is trending upwards which is good. More importantly the green line which is the daily SMA200 is way above the SMA200 for the weekly. This is a strong indication that the S&P 500 is a really good option to be trading.<\/p>\n\n\n\n
Merging the Daily and Hourly datasets<\/h2>\n\n\n\n The question now is when do we get in and out of our positions. To do this we’ll need to compare the SMA200 for the Daily and the SAM for the Hourly.<\/p>\n\n\n\n
I want to merge the daily SMA200 dataframe with the hourly SMA200 dataframe. This is not as straightforward as it sounds. The index for the daily is only a date, but the index for the hourly is a date and time. This will prevent this from working without some work.<\/p>\n\n\n\n
The way I resolved this is to create a new dataframe called, \u201cdf_hourly2<\/strong>\u201d with a copy of the, \u201cdf_hourly<\/strong>\u201d dataframe. I created a new feature called \u201cJoinOnDate<\/strong>\u201d with only the date from the date and time index. I also stored the current index for later in \u201cStoreIndex<\/strong>\u201d.<\/p>\n\n\n\n \n
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df_hourly2 = df_hourly.copy()\n df_hourly2[\"JoinOnDate\"] = df_hourly.index.date\n df_hourly2[\"JoinOnDate\"] = pd.to_datetime(df_hourly2[\"JoinOnDate\"]).dt.date\n df_hourly2[\"StoreIndex\"] = df_hourly.index\n print(df_hourly2)\n<\/code><\/pre>\n\n <\/div>\n \n
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df_daily2 = df_daily.copy()\n df_daily2[\"JoinOnDate\"] = df_daily2.index\n df_daily2[\"JoinOnDate\"] = pd.to_datetime(df_daily2[\"JoinOnDate\"]).dt.date\n print(df_daily2)\n<\/code><\/pre>\n\n <\/div>\n \n
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df_daily_hourly = df_hourly2.merge(df_daily2[[\"JoinOnDate\", \"Daily SMA200\"]], on=\"JoinOnDate\", how=\"left\").set_index(\"StoreIndex\")\n df_daily_hourly.index.name = \"Date\"\n df_daily_hourly.drop([\"JoinOnDate\"], axis=1, inplace=True)\n print(df_daily_hourly)<\/code><\/pre>\n\n <\/div>\n \n
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df_plot = df_daily_hourly.dropna().loc[\"2023\", [\"close\", \"Daily SMA200\", \"Hourly SMA200\"]]\n df_plot = df_plot[df_plot[\"close\"] != 0]\n df_plot.rename(columns={\"close\": \"Close\"}, inplace=True)\n df_plot.plot(title=\"S&P 500 Weekly SMA 200 vs. Daily SMA 200\", kind=\"line\", color=[\"black\", \"green\", \"blue\"], figsize=(36, 12))\n plt.show()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n The blue line is the Hourly SMA200, and the green line is the Daily SMA200. As you can see the Hourly SMA200 is above the Daily SMA200 which is really good. You can also see visually when the Daily crossed above the Weekly SMA200 which resulted in a strong upward trend on the price. If you had bought mid-June 2023 when the crossover happened, you would be in a pretty decent position now.<\/p>\n\n\n\n
In summary<\/h2>\n\n\n\n Register & Get Data<\/span><\/a><\/p>\n\n\n\nBuy signal:<\/p>\n\n\n\n