{"id":20021773,"url":"https://github.com/ndleah/bitcoin-prices-analysis","last_synced_at":"2025-05-05T01:31:00.200Z","repository":{"id":105088988,"uuid":"374398283","full_name":"ndleah/bitcoin-prices-analysis","owner":"ndleah","description":"šŸ’ø Bitcoin trading 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Badge](https://img.shields.io/static/v1?label=%F0%9F%8C%9F\u0026message=If%20Useful\u0026style=style=flat\u0026color=BC4E99)\n![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)\n[![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/ndleah)\n[![View Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/ndleah?tab=repositories)\n\n# Bitcoin Prices Analysis \u003cimg src=\"https://i.pinimg.com/736x/8b/f4/cb/8bf4cba38bf9ce86c18183e62251d6f5.jpg\" align=\"right\" width=\"120\" /\u003e\n\u003e This case study is contained within the [Serious SQL](https://www.datawithdanny.com) by [Danny Ma](https://www.linkedin.com/in/datawithdanny/).\n\n \u003cbr /\u003e \n\n\n\n## šŸ“• Table of contents\n\u003c!--ts--\u003e\n * šŸ› ļø [Overview](#ļø-overview)\n * šŸš€ [Solutions](#solution)\n * [Data Exploration](#data-exploration)\n * [Weekly Volume Comparison Analysis](#weekly-volume-comparison-analysis)\n * [Statistical Analysis](#statistical-analysis)\n\n \n## šŸ› ļø Overview\n\n---\n## šŸš€ Solution\n### Data Exploration\nšŸ“ **Identify Null Rows**\n```sql \nSELECT *\nFROM trading.daily_btc\nWHERE (\n open_price + high_price + low_price + \n close_price + adjusted_close_price + volume \n) IS NULL;\n```\n\n|market_date |open_price |high_price |low_price |close_price |adjusted_close_price|volume |\n|------------------------|------------|------------|------------|------------|--------------------|------------|\n|2020-04-17T00:00:00.000Z|null |null |null |null |null |null |\n|2020-10-09T00:00:00.000Z|null |null |null |null |null |null |\n|2020-10-12T00:00:00.000Z|null |null |null |null |null |null |\n|2020-10-13T00:00:00.000Z|null |null |null |null |null |null |\n\nšŸ“ **Filling \u0026 Update Null Values**\n```sql\nWITH april_17_data AS (\n SELECT\n market_date,\n open_price,\n LAG(open_price) OVER (ORDER BY market_date) AS lag_open_price\nFROM trading.daily_btc\nWHERE market_date BETWEEN ('2020-04-17'::DATE - 1) AND ('2020-04-17'::DATE + 1)\n)\nSELECT\n market_date,\n open_price,\n lag_open_price,\n COALESCE(open_price, lag_open_price) AS coalesce_open_price\nFROM april_17_data;\n```\n\n|market_date |open_price |lag_open_price|coalesce_open_price|\n|------------------------|------------|--------------|-------------------|\n|2020-04-16T00:00:00.000Z|6640.454102 |null |6640.454102 |\n|2020-04-17T00:00:00.000Z|null |6640.454102 |6640.454102 |\n|2020-04-18T00:00:00.000Z|7092.291504 |null |7092.291504 |\n\nšŸ“ **Update Tables**\n```sql\nDROP TABLE IF EXISTS updated_daily_btc;\nCREATE TEMP TABLE updated_daily_btc AS\nSELECT\n market_date,\n COALESCE(\n open_price,\n LAG(open_price, 1) OVER (ORDER BY market_date),\n LAG(open_price, 2) OVER (ORDER BY market_date)\n ) AS open_price,\n COALESCE(\n high_price,\n LAG(high_price, 1) OVER (ORDER BY market_date),\n LAG(high_price, 2) OVER (ORDER BY market_date)\n ) AS high_price,\n COALESCE(\n low_price,\n LAG(low_price, 1) OVER (ORDER BY market_date),\n LAG(low_price, 2) OVER (ORDER BY market_date)\n ) AS low_price,\n COALESCE(\n close_price,\n LAG(close_price, 1) OVER (ORDER BY market_date),\n LAG(close_price, 2) OVER (ORDER BY market_date)\n ) AS close_price,\n COALESCE(\n adjusted_close_price,\n LAG(adjusted_close_price, 1) OVER (ORDER BY market_date),\n LAG(adjusted_close_price, 2) OVER (ORDER BY market_date)\n ) AS adjusted_close_price,\n COALESCE(\n volume,\n LAG(volume, 1) OVER (ORDER BY market_date),\n LAG(volume, 2) OVER (ORDER BY market_date)\n ) AS volume\nFROM trading.daily_btc;\n\nSELECT *\nFROM updated_daily_btc\nWHERE market_date IN (\n '2020-04-17',\n '2020-10-09',\n '2020-10-12',\n '2020-10-13'\n);\n```\n\n|market_date |open_price |high_price |low_price |close_price |adjusted_close_price|volume |\n|------------------------|------------|------------|------------|------------|--------------------|-----------|\n|2020-04-17T00:00:00.000Z|6640.454102 |7134.450684 |6555.504395 |7116.804199 |7116.804199 |46783242377|\n|2020-10-09T00:00:00.000Z|10677.625000|10939.799805|10569.823242|10923.627930|10923.627930 |21962121001|\n|2020-10-12T00:00:00.000Z|11296.082031|11428.813477|11288.627930|11384.181641|11384.181641 |19968627060|\n|2020-10-13T00:00:00.000Z|11296.082031|11428.813477|11288.627930|11384.181641|11384.181641 |19968627060|\n\n### Weekly Volume Comparison Analysis\nThe following are a set of questions that i would use to practice using SQL window functions as well as discover insights in this case study:\n\n1. What is the average daily volume of Bitcoin for the last 7 days?\n2. Create a 1/0 flag if a specific day is higher than the last 7 days volume average\n3. What is the percentage of weeks (starting on a Monday) where there are 4 or more days with increased volume?\n4. How many high volume weeks are there broken down by year for the weeks with 5-7 days above the 7 day volume average excluding 2021?\n\nšŸ“ **Average Volumes**\n\u003e This section contains solution for Question 1 and 2\n```sql\nWITH window_calculations AS (\nSELECT\n market_date,\n volume,\n ROUND(\n AVG(volume) OVER (\n ORDER BY market_date\n RANGE BETWEEN '7 DAYS' PRECEDING AND '1 DAY' PRECEDING)\n ) AS past_weekly_avg_volume\nFROM updated_daily_btc\n)\nSELECT\n market_date,\n volume,\n past_weekly_avg_volume,\n CASE \n WHEN volume \u003e past_weekly_avg_volume THEN 1\n ELSE 0\n END AS volume_flag\nFROM window_calculations\nORDER BY market_date DESC\nLIMIT 10;\n```\n\n|market_date |volume |past_weekly_avg_volume|volume_flag|\n|------------------------|------------|----------------------|-----------|\n|2021-02-24T00:00:00.000Z|88364793856 |73509817753 |1 |\n|2021-02-23T00:00:00.000Z|106102492824|69359402048 |1 |\n|2021-02-22T00:00:00.000Z|92052420332 |67219042453 |1 |\n|2021-02-21T00:00:00.000Z|51897585191 |69983483887 |0 |\n|2021-02-20T00:00:00.000Z|68145460026 |70284197619 |0 |\n|2021-02-19T00:00:00.000Z|63495496918 |72149846802 |0 |\n|2021-02-18T00:00:00.000Z|52054723579 |76340445121 |0 |\n|2021-02-17T00:00:00.000Z|80820545404 |77266237191 |1 |\n|2021-02-16T00:00:00.000Z|77049582886 |79374846334 |0 |\n|2021-02-15T00:00:00.000Z|77069903166 |82860177694 |0 |\n\n šŸ“ **High Volume Weeks**\n\u003e This section contains solution for Question 3 and 4\n1. **Break down by week**\n```sql\nWITH window_calculations AS (\nSELECT\n market_date,\n volume,\n ROUND(\n AVG(volume) OVER (\n ORDER BY market_date\n RANGE BETWEEN '7 DAYS' PRECEDING AND '1 DAY' PRECEDING)\n ) AS past_weekly_avg_volume\nFROM updated_daily_btc\n),\n--generate the date\ndate_calculations AS (\nSELECT\n market_date,\n DATE_TRUNC('week', market_date)::DATE start_of_week,\n volume,\n CASE\n WHEN volume \u003e past_weekly_avg_volume THEN 1\n ELSE 0\n END AS volume_flag\nFROM window_calculations\n),\n--aggregate the metrics\naggregated_weeks AS (\nSELECT\n start_of_week,\n SUM(volume_flag) AS weekly_high_volume_days\n FROM date_calculations\n GROUP BY start_of_week\n)\n--calculate the percentage\nSELECT\n weekly_high_volume_days,\n ROUND (\n 100 * COUNT(*)/SUM(COUNT(*)) OVER (), \n 1) AS percentage_of_weeks\nFROM aggregated_weeks\nGROUP BY weekly_high_volume_days\nORDER BY weekly_high_volume_days;\n```\n\n|weekly_high_volume_days |percentage_of_weeks|\n|------------------------|-------------------|\n|0 |6.23 |\n|1 |13.65 |\n|2 |20.47 |\n|3 |20.47 |\n|4 |18.99 |\n|5 |11.87 |\n|6 |6.23 |\n|7 |2.08 |\n\n**Break down by year**\n\n```sql\nWITH window_calculations AS (\nSELECT\n market_date,\n volume,\n ROUND(\n AVG(volume) OVER (\n ORDER BY market_date\n RANGE BETWEEN '7 DAYS' PRECEDING AND '1 DAY' PRECEDING)\n ) AS past_weekly_avg_volume\nFROM updated_daily_btc\n),\n--generate the date\ndate_calculations AS (\nSELECT\n market_date,\n DATE_TRUNC('week', market_date)::DATE start_of_week,\n volume,\n CASE\n WHEN volume \u003e past_weekly_avg_volume THEN 1\n ELSE 0\n END AS volume_flag\nFROM window_calculations\n),\n--aggregate the metrics\naggregated_weeks AS (\nSELECT\n start_of_week,\n SUM(volume_flag) AS weekly_high_volume_days\n FROM date_calculations\n GROUP BY start_of_week\n)\n--calculate the percentage (by year)\nSELECT\n EXTRACT(YEAR FROM start_of_week) AS market_year,\n COUNT(*) AS high_volume_weeks,\n ROUND(100 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage_of_total\nFROM aggregated_weeks\nWHERE weekly_high_volume_days \u003e= 5\nAND start_of_week \u003c '2021-01-01'::DATE\nGROUP BY 1\nORDER BY 1;\n```\n\n|market_year |high_volume_weeks|percentage_of_total|\n|------------------------|-----------------|-------------------|\n|2014 |2 |2.99 |\n|2015 |3 |4.48 |\n|2016 |13 |19.40 |\n|2017 |17 |25.37 |\n|2018 |8 |11.94 |\n|2019 |11 |16.42 |\n|2020 |13 |19.40 |\n\n### Statistical Analysis\nšŸ“ **Simple moving average**\n\nFor the next exercise - we will be calculating metrics not just a single window function but multiple in the same query.\n\nFor the following time windows: **14, 28, 60, 150 days** - calculate the following metrics for the close_price column:\n\n* Moving average\n* Moving standard deviation\n* The maximum and minimum values\n\n```sql\nWITH base_data AS (\n SELECT\n market_date,\n close_price,\n -- averages\n ROUND(AVG(close_price) OVER w_14) AS avg_14,\n ROUND(AVG(close_price) OVER w_28) AS avg_28,\n ROUND(AVG(close_price) OVER w_60) AS avg_60,\n ROUND(AVG(close_price) OVER w_150) AS avg_150,\n -- standard deviation\n ROUND(STDDEV(close_price) OVER w_14) AS std_14,\n ROUND(STDDEV(close_price) OVER w_28) AS std_28,\n ROUND(STDDEV(close_price) OVER w_60) AS std_60,\n ROUND(STDDEV(close_price) OVER w_150) AS std_150,\n -- max\n ROUND(MAX(close_price) OVER w_14) AS max_14,\n ROUND(MAX(close_price) OVER w_28) AS max_28,\n ROUND(MAX(close_price) OVER w_60) AS max_60,\n ROUND(MAX(close_price) OVER w_150) AS max_150,\n -- min\n ROUND(MIN(close_price) OVER w_14) AS min_14,\n ROUND(MIN(close_price) OVER w_28) AS min_28,\n ROUND(MIN(close_price) OVER w_60) AS min_60,\n ROUND(MIN(close_price) OVER w_150) AS min_150\n FROM updated_daily_btc\n WINDOW\n w_14 AS (ORDER BY MARKET_DATE RANGE BETWEEN '14 DAYS' PRECEDING AND '1 DAY' PRECEDING),\n w_28 AS (ORDER BY MARKET_DATE RANGE BETWEEN '28 DAYS' PRECEDING AND '1 DAY' PRECEDING),\n w_60 AS (ORDER BY MARKET_DATE RANGE BETWEEN '60 DAYS' PRECEDING AND '1 DAY' PRECEDING),\n w_150 AS (ORDER BY MARKET_DATE RANGE BETWEEN '150 DAYS' PRECEDING AND '1 DAY' PRECEDING)\n)\nSELECT\n market_date,\n close_price,\n CASE\n WHEN close_price BETWEEN\n (avg_14 - 2 * std_14) AND (avg_14 + 2 * std_14)\n THEN 0\n ELSE 1\n END AS outlier_14,\n CASE\n WHEN close_price BETWEEN\n (avg_28 - 2 * std_28) AND (avg_28 + 2 * std_28)\n THEN 0\n ELSE 1\n END AS outlier_28,\n CASE\n WHEN close_price BETWEEN\n (avg_60 - 2 * std_60) AND (avg_60 + 2 * std_60)\n THEN 0\n ELSE 1\n END AS outlier_60,\n CASE\n WHEN close_price BETWEEN\n (avg_150 - 2 * std_150) AND (avg_150 + 2 * std_150)\n THEN 0\n ELSE 1\n END AS outlier_150\nFROM base_data\nORDER BY market_date DESC\nLIMIT 10;\n;\n```\n\n|market_date |close_price |outlier_14|outlier_28|outlier_60|outlier_150|\n|------------------------|------------|----------|----------|----------|-----------|\n|2021-02-24T00:00:00.000Z|50460.234375|0 |0 |0 |1 |\n|2021-02-23T00:00:00.000Z|48824.425781|0 |0 |0 |0 |\n|2021-02-22T00:00:00.000Z|54207.320313|0 |0 |1 |1 |\n|2021-02-21T00:00:00.000Z|57539.945313|1 |0 |1 |1 |\n|2021-02-20T00:00:00.000Z|56099.519531|0 |0 |1 |1 |\n|2021-02-19T00:00:00.000Z|55888.132813|1 |1 |1 |1 |\n|2021-02-18T00:00:00.000Z|51679.796875|0 |0 |1 |1 |\n|2021-02-17T00:00:00.000Z|52149.007813|0 |1 |1 |1 |\n|2021-02-16T00:00:00.000Z|49199.871094|0 |0 |1 |1 |\n|2021-02-15T00:00:00.000Z|47945.058594|0 |0 |1 |1 |\n\n**Finding**\n\nThere are 1 values for 60 and 150 day outliers for the majority of the rows or is it expected.\n\n**Illustration**\n\u003cp align=\"center\"\u003e\n \u003cimg width=\"60%\" height=\"60%\" src=\"/IMG/SMA.png\"\u003e\n\u003c/p\u003e\n\nšŸ“ **Weighted moving average**\n\nIn this section, we were tasked by a demanding Cryptocurrency trading client who required custom weights to be applied using the following combination of simple moving averages:\n \n|Time Period|Weight Factor|\n|---------- |------------ |\n|1-14 days |0.5 |\n|15-28 days |0.3 |\n|29-60 days |0.15 |\n|61-150 days|0.05 |\n\n```sql\nWITH base_data AS (\n SELECT\n market_date,\n ROUND(close_price, 2) AS close_price,\n ROUND(AVG(close_price) OVER w_1_to_14) AS avg_1_to_14,\n ROUND(AVG(close_price) OVER w_15_28) AS avg_15_28,\n ROUND(AVG(close_price) OVER w_29_60) AS avg_29_60,\n ROUND(AVG(close_price) OVER w_61_150) AS avg_61_150\nFROM updated_daily_btc\nWINDOW\n w_1_to_14 AS (ORDER BY MARKET_DATE RANGE BETWEEN\n '14 DAYS' PRECEDING AND '1 DAY' PRECEDING),\n w_15_28 AS (ORDER BY MARKET_DATE RANGE BETWEEN\n '28 DAYS' PRECEDING AND '15 DAY' PRECEDING),\n w_29_60 AS (ORDER BY MARKET_DATE RANGE BETWEEN\n '60 DAYS' PRECEDING AND '29 DAY' PRECEDING),\n w_61_150 AS (ORDER BY MARKET_DATE RANGE BETWEEN\n '150 DAYS' PRECEDING AND '61 DAY' PRECEDING)\n)\nSELECT\n market_date,\n close_price,\n 0.5 * avg_1_to_14 + 0.3 * avg_15_28 + 0.15 * avg_29_60 + 0.05 * avg_61_150 AS custom_moving_avg\nFROM base_data\nORDER BY market_date DESC\nLIMIT 10;\n```\n\n|market_date |close_price |custom_moving_avg|\n|------------------------|------------|-----------------|\n|2021-02-24T00:00:00.000Z|50460.23 |42249.35 |\n|2021-02-23T00:00:00.000Z|48824.43 |41822.85 |\n|2021-02-22T00:00:00.000Z|54207.32 |41192.25 |\n|2021-02-21T00:00:00.000Z|57539.95 |40335.75 |\n|2021-02-20T00:00:00.000Z|56099.52 |39534.15 |\n|2021-02-19T00:00:00.000Z|55888.13 |38735.40 |\n|2021-02-18T00:00:00.000Z|51679.80 |38036.50 |\n|2021-02-17T00:00:00.000Z|52149.01 |37408.70 |\n|2021-02-16T00:00:00.000Z|49199.87 |36864.40 |\n|2021-02-15T00:00:00.000Z|47945.06 |36344.80 |\n\nšŸ“ **Exponential Weighted Moving Average (EWMA)**\n```sql\nDROP TABLE IF EXISTS base_table;\nCREATE TEMP TABLE base_table AS\nSELECT\n market_date,\n ROUND(close_price, 2) AS close_price,\n ROUND(AVG(close_price) OVER w_1_to_14) AS sma_14,\n ROW_NUMBER() OVER w_1_to_14 AS _row_number\nFROM updated_daily_btc\nWINDOW\n w_1_to_14 AS (ORDER BY MARKET_DATE RANGE BETWEEN\n '14 DAYS' PRECEDING AND '1 DAY' PRECEDING);\n\nDROP TABLE IF EXISTS ewma_output;\nCREATE TEMP TABLE ewma_output AS\nWITH RECURSIVE output_table\n(market_date, close_price, sma_14, ewma_14, _row_number)\nAS (\n\n-- The 1st query: we set initial output row using _row_number = 15\n SELECT\n market_date,\n close_price,\n sma_14,\n sma_14 AS ewma_14,\n _row_number\n FROM base_table\n WHERE _row_number = 15\n\n UNION ALL\n\n -- The 2nd query: we need to \"shift\" our output by 1 row\n SELECT\n base_table.market_date,\n base_table.close_price,\n base_table.sma_14,\n -- Let's round out ewma_14 record to 2 decimal places\n ROUND(\n 0.13 * base_table.sma_14 + (1 - 0.13) * output_table.ewma_14,\n 2\n ) AS ewma_14,\n base_table._row_number\n FROM output_table\n INNER JOIN base_table\n ON output_table._row_number + 1 = base_table._row_number\n AND base_table._row_number \u003e 15\n)\nSELECT * FROM output_table;\n\n-- Pivot data for visualisation\nSELECT\n market_date,\n 'SMA' AS metric_name,\n sma_14 AS metric_value\nFROM ewma_output\nUNION\nSELECT\n market_date,\n 'EWMA' AS metric_name,\n ewma_14 AS metric_value\nFROM ewma_output;\n\nSELECT\n market_date,\n 'Price' AS metric_name,\n close_price AS metric_value\nFROM ewma_output\nORDER BY market_date, metric_name;\n``` \n\n**Illustration**\n\u003cp align=\"center\"\u003e\n \u003cimg width=\"60%\" height=\"60%\" src=\"IMG/EWMA.png\"\u003e\n\u003c/p\u003e\n\n## āœØ Contribution\n\nContributions, issues, and feature requests are welcome!\n\nTo contribute to this project, see the GitHub documentation on **[creating a pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request)**.\n\n\n## šŸ‘ Support\n\nGive a ā­ļø if you like this project!\n\n\u003cp\u003e\u0026copy; 2021 Leah Nguyen\u003c/p\u003e\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fndleah%2Fbitcoin-prices-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fndleah%2Fbitcoin-prices-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fndleah%2Fbitcoin-prices-analysis/lists"}