https://github.com/ndleah/bitcoin-prices-analysis
💸 Bitcoin trading analysis
https://github.com/ndleah/bitcoin-prices-analysis
data-analysis data-expl data-science sqlite window-functions
Last synced: about 1 year ago
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💸 Bitcoin trading analysis
- Host: GitHub
- URL: https://github.com/ndleah/bitcoin-prices-analysis
- Owner: ndleah
- Created: 2021-06-06T15:39:22.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-09-14T17:52:23.000Z (over 4 years ago)
- Last Synced: 2025-04-08T14:45:49.662Z (about 1 year ago)
- Topics: data-analysis, data-expl, data-science, sqlite, window-functions
- Language: SQL
- Homepage:
- Size: 1.11 MB
- Stars: 7
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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# Bitcoin Prices Analysis 
> This case study is contained within the [Serious SQL](https://www.datawithdanny.com) by [Danny Ma](https://www.linkedin.com/in/datawithdanny/).
## 📕 Table of contents
* 🛠️ [Overview](#️-overview)
* 🚀 [Solutions](#solution)
* [Data Exploration](#data-exploration)
* [Weekly Volume Comparison Analysis](#weekly-volume-comparison-analysis)
* [Statistical Analysis](#statistical-analysis)
## 🛠️ Overview
---
## 🚀 Solution
### Data Exploration
📍 **Identify Null Rows**
```sql
SELECT *
FROM trading.daily_btc
WHERE (
open_price + high_price + low_price +
close_price + adjusted_close_price + volume
) IS NULL;
```
|market_date |open_price |high_price |low_price |close_price |adjusted_close_price|volume |
|------------------------|------------|------------|------------|------------|--------------------|------------|
|2020-04-17T00:00:00.000Z|null |null |null |null |null |null |
|2020-10-09T00:00:00.000Z|null |null |null |null |null |null |
|2020-10-12T00:00:00.000Z|null |null |null |null |null |null |
|2020-10-13T00:00:00.000Z|null |null |null |null |null |null |
📍 **Filling & Update Null Values**
```sql
WITH april_17_data AS (
SELECT
market_date,
open_price,
LAG(open_price) OVER (ORDER BY market_date) AS lag_open_price
FROM trading.daily_btc
WHERE market_date BETWEEN ('2020-04-17'::DATE - 1) AND ('2020-04-17'::DATE + 1)
)
SELECT
market_date,
open_price,
lag_open_price,
COALESCE(open_price, lag_open_price) AS coalesce_open_price
FROM april_17_data;
```
|market_date |open_price |lag_open_price|coalesce_open_price|
|------------------------|------------|--------------|-------------------|
|2020-04-16T00:00:00.000Z|6640.454102 |null |6640.454102 |
|2020-04-17T00:00:00.000Z|null |6640.454102 |6640.454102 |
|2020-04-18T00:00:00.000Z|7092.291504 |null |7092.291504 |
📍 **Update Tables**
```sql
DROP TABLE IF EXISTS updated_daily_btc;
CREATE TEMP TABLE updated_daily_btc AS
SELECT
market_date,
COALESCE(
open_price,
LAG(open_price, 1) OVER (ORDER BY market_date),
LAG(open_price, 2) OVER (ORDER BY market_date)
) AS open_price,
COALESCE(
high_price,
LAG(high_price, 1) OVER (ORDER BY market_date),
LAG(high_price, 2) OVER (ORDER BY market_date)
) AS high_price,
COALESCE(
low_price,
LAG(low_price, 1) OVER (ORDER BY market_date),
LAG(low_price, 2) OVER (ORDER BY market_date)
) AS low_price,
COALESCE(
close_price,
LAG(close_price, 1) OVER (ORDER BY market_date),
LAG(close_price, 2) OVER (ORDER BY market_date)
) AS close_price,
COALESCE(
adjusted_close_price,
LAG(adjusted_close_price, 1) OVER (ORDER BY market_date),
LAG(adjusted_close_price, 2) OVER (ORDER BY market_date)
) AS adjusted_close_price,
COALESCE(
volume,
LAG(volume, 1) OVER (ORDER BY market_date),
LAG(volume, 2) OVER (ORDER BY market_date)
) AS volume
FROM trading.daily_btc;
SELECT *
FROM updated_daily_btc
WHERE market_date IN (
'2020-04-17',
'2020-10-09',
'2020-10-12',
'2020-10-13'
);
```
|market_date |open_price |high_price |low_price |close_price |adjusted_close_price|volume |
|------------------------|------------|------------|------------|------------|--------------------|-----------|
|2020-04-17T00:00:00.000Z|6640.454102 |7134.450684 |6555.504395 |7116.804199 |7116.804199 |46783242377|
|2020-10-09T00:00:00.000Z|10677.625000|10939.799805|10569.823242|10923.627930|10923.627930 |21962121001|
|2020-10-12T00:00:00.000Z|11296.082031|11428.813477|11288.627930|11384.181641|11384.181641 |19968627060|
|2020-10-13T00:00:00.000Z|11296.082031|11428.813477|11288.627930|11384.181641|11384.181641 |19968627060|
### Weekly Volume Comparison Analysis
The 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:
1. What is the average daily volume of Bitcoin for the last 7 days?
2. Create a 1/0 flag if a specific day is higher than the last 7 days volume average
3. What is the percentage of weeks (starting on a Monday) where there are 4 or more days with increased volume?
4. 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?
📍 **Average Volumes**
> This section contains solution for Question 1 and 2
```sql
WITH window_calculations AS (
SELECT
market_date,
volume,
ROUND(
AVG(volume) OVER (
ORDER BY market_date
RANGE BETWEEN '7 DAYS' PRECEDING AND '1 DAY' PRECEDING)
) AS past_weekly_avg_volume
FROM updated_daily_btc
)
SELECT
market_date,
volume,
past_weekly_avg_volume,
CASE
WHEN volume > past_weekly_avg_volume THEN 1
ELSE 0
END AS volume_flag
FROM window_calculations
ORDER BY market_date DESC
LIMIT 10;
```
|market_date |volume |past_weekly_avg_volume|volume_flag|
|------------------------|------------|----------------------|-----------|
|2021-02-24T00:00:00.000Z|88364793856 |73509817753 |1 |
|2021-02-23T00:00:00.000Z|106102492824|69359402048 |1 |
|2021-02-22T00:00:00.000Z|92052420332 |67219042453 |1 |
|2021-02-21T00:00:00.000Z|51897585191 |69983483887 |0 |
|2021-02-20T00:00:00.000Z|68145460026 |70284197619 |0 |
|2021-02-19T00:00:00.000Z|63495496918 |72149846802 |0 |
|2021-02-18T00:00:00.000Z|52054723579 |76340445121 |0 |
|2021-02-17T00:00:00.000Z|80820545404 |77266237191 |1 |
|2021-02-16T00:00:00.000Z|77049582886 |79374846334 |0 |
|2021-02-15T00:00:00.000Z|77069903166 |82860177694 |0 |
📍 **High Volume Weeks**
> This section contains solution for Question 3 and 4
1. **Break down by week**
```sql
WITH window_calculations AS (
SELECT
market_date,
volume,
ROUND(
AVG(volume) OVER (
ORDER BY market_date
RANGE BETWEEN '7 DAYS' PRECEDING AND '1 DAY' PRECEDING)
) AS past_weekly_avg_volume
FROM updated_daily_btc
),
--generate the date
date_calculations AS (
SELECT
market_date,
DATE_TRUNC('week', market_date)::DATE start_of_week,
volume,
CASE
WHEN volume > past_weekly_avg_volume THEN 1
ELSE 0
END AS volume_flag
FROM window_calculations
),
--aggregate the metrics
aggregated_weeks AS (
SELECT
start_of_week,
SUM(volume_flag) AS weekly_high_volume_days
FROM date_calculations
GROUP BY start_of_week
)
--calculate the percentage
SELECT
weekly_high_volume_days,
ROUND (
100 * COUNT(*)/SUM(COUNT(*)) OVER (),
1) AS percentage_of_weeks
FROM aggregated_weeks
GROUP BY weekly_high_volume_days
ORDER BY weekly_high_volume_days;
```
|weekly_high_volume_days |percentage_of_weeks|
|------------------------|-------------------|
|0 |6.23 |
|1 |13.65 |
|2 |20.47 |
|3 |20.47 |
|4 |18.99 |
|5 |11.87 |
|6 |6.23 |
|7 |2.08 |
**Break down by year**
```sql
WITH window_calculations AS (
SELECT
market_date,
volume,
ROUND(
AVG(volume) OVER (
ORDER BY market_date
RANGE BETWEEN '7 DAYS' PRECEDING AND '1 DAY' PRECEDING)
) AS past_weekly_avg_volume
FROM updated_daily_btc
),
--generate the date
date_calculations AS (
SELECT
market_date,
DATE_TRUNC('week', market_date)::DATE start_of_week,
volume,
CASE
WHEN volume > past_weekly_avg_volume THEN 1
ELSE 0
END AS volume_flag
FROM window_calculations
),
--aggregate the metrics
aggregated_weeks AS (
SELECT
start_of_week,
SUM(volume_flag) AS weekly_high_volume_days
FROM date_calculations
GROUP BY start_of_week
)
--calculate the percentage (by year)
SELECT
EXTRACT(YEAR FROM start_of_week) AS market_year,
COUNT(*) AS high_volume_weeks,
ROUND(100 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage_of_total
FROM aggregated_weeks
WHERE weekly_high_volume_days >= 5
AND start_of_week < '2021-01-01'::DATE
GROUP BY 1
ORDER BY 1;
```
|market_year |high_volume_weeks|percentage_of_total|
|------------------------|-----------------|-------------------|
|2014 |2 |2.99 |
|2015 |3 |4.48 |
|2016 |13 |19.40 |
|2017 |17 |25.37 |
|2018 |8 |11.94 |
|2019 |11 |16.42 |
|2020 |13 |19.40 |
### Statistical Analysis
📍 **Simple moving average**
For the next exercise - we will be calculating metrics not just a single window function but multiple in the same query.
For the following time windows: **14, 28, 60, 150 days** - calculate the following metrics for the close_price column:
* Moving average
* Moving standard deviation
* The maximum and minimum values
```sql
WITH base_data AS (
SELECT
market_date,
close_price,
-- averages
ROUND(AVG(close_price) OVER w_14) AS avg_14,
ROUND(AVG(close_price) OVER w_28) AS avg_28,
ROUND(AVG(close_price) OVER w_60) AS avg_60,
ROUND(AVG(close_price) OVER w_150) AS avg_150,
-- standard deviation
ROUND(STDDEV(close_price) OVER w_14) AS std_14,
ROUND(STDDEV(close_price) OVER w_28) AS std_28,
ROUND(STDDEV(close_price) OVER w_60) AS std_60,
ROUND(STDDEV(close_price) OVER w_150) AS std_150,
-- max
ROUND(MAX(close_price) OVER w_14) AS max_14,
ROUND(MAX(close_price) OVER w_28) AS max_28,
ROUND(MAX(close_price) OVER w_60) AS max_60,
ROUND(MAX(close_price) OVER w_150) AS max_150,
-- min
ROUND(MIN(close_price) OVER w_14) AS min_14,
ROUND(MIN(close_price) OVER w_28) AS min_28,
ROUND(MIN(close_price) OVER w_60) AS min_60,
ROUND(MIN(close_price) OVER w_150) AS min_150
FROM updated_daily_btc
WINDOW
w_14 AS (ORDER BY MARKET_DATE RANGE BETWEEN '14 DAYS' PRECEDING AND '1 DAY' PRECEDING),
w_28 AS (ORDER BY MARKET_DATE RANGE BETWEEN '28 DAYS' PRECEDING AND '1 DAY' PRECEDING),
w_60 AS (ORDER BY MARKET_DATE RANGE BETWEEN '60 DAYS' PRECEDING AND '1 DAY' PRECEDING),
w_150 AS (ORDER BY MARKET_DATE RANGE BETWEEN '150 DAYS' PRECEDING AND '1 DAY' PRECEDING)
)
SELECT
market_date,
close_price,
CASE
WHEN close_price BETWEEN
(avg_14 - 2 * std_14) AND (avg_14 + 2 * std_14)
THEN 0
ELSE 1
END AS outlier_14,
CASE
WHEN close_price BETWEEN
(avg_28 - 2 * std_28) AND (avg_28 + 2 * std_28)
THEN 0
ELSE 1
END AS outlier_28,
CASE
WHEN close_price BETWEEN
(avg_60 - 2 * std_60) AND (avg_60 + 2 * std_60)
THEN 0
ELSE 1
END AS outlier_60,
CASE
WHEN close_price BETWEEN
(avg_150 - 2 * std_150) AND (avg_150 + 2 * std_150)
THEN 0
ELSE 1
END AS outlier_150
FROM base_data
ORDER BY market_date DESC
LIMIT 10;
;
```
|market_date |close_price |outlier_14|outlier_28|outlier_60|outlier_150|
|------------------------|------------|----------|----------|----------|-----------|
|2021-02-24T00:00:00.000Z|50460.234375|0 |0 |0 |1 |
|2021-02-23T00:00:00.000Z|48824.425781|0 |0 |0 |0 |
|2021-02-22T00:00:00.000Z|54207.320313|0 |0 |1 |1 |
|2021-02-21T00:00:00.000Z|57539.945313|1 |0 |1 |1 |
|2021-02-20T00:00:00.000Z|56099.519531|0 |0 |1 |1 |
|2021-02-19T00:00:00.000Z|55888.132813|1 |1 |1 |1 |
|2021-02-18T00:00:00.000Z|51679.796875|0 |0 |1 |1 |
|2021-02-17T00:00:00.000Z|52149.007813|0 |1 |1 |1 |
|2021-02-16T00:00:00.000Z|49199.871094|0 |0 |1 |1 |
|2021-02-15T00:00:00.000Z|47945.058594|0 |0 |1 |1 |
**Finding**
There are 1 values for 60 and 150 day outliers for the majority of the rows or is it expected.
**Illustration**
📍 **Weighted moving average**
In 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:
|Time Period|Weight Factor|
|---------- |------------ |
|1-14 days |0.5 |
|15-28 days |0.3 |
|29-60 days |0.15 |
|61-150 days|0.05 |
```sql
WITH base_data AS (
SELECT
market_date,
ROUND(close_price, 2) AS close_price,
ROUND(AVG(close_price) OVER w_1_to_14) AS avg_1_to_14,
ROUND(AVG(close_price) OVER w_15_28) AS avg_15_28,
ROUND(AVG(close_price) OVER w_29_60) AS avg_29_60,
ROUND(AVG(close_price) OVER w_61_150) AS avg_61_150
FROM updated_daily_btc
WINDOW
w_1_to_14 AS (ORDER BY MARKET_DATE RANGE BETWEEN
'14 DAYS' PRECEDING AND '1 DAY' PRECEDING),
w_15_28 AS (ORDER BY MARKET_DATE RANGE BETWEEN
'28 DAYS' PRECEDING AND '15 DAY' PRECEDING),
w_29_60 AS (ORDER BY MARKET_DATE RANGE BETWEEN
'60 DAYS' PRECEDING AND '29 DAY' PRECEDING),
w_61_150 AS (ORDER BY MARKET_DATE RANGE BETWEEN
'150 DAYS' PRECEDING AND '61 DAY' PRECEDING)
)
SELECT
market_date,
close_price,
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
FROM base_data
ORDER BY market_date DESC
LIMIT 10;
```
|market_date |close_price |custom_moving_avg|
|------------------------|------------|-----------------|
|2021-02-24T00:00:00.000Z|50460.23 |42249.35 |
|2021-02-23T00:00:00.000Z|48824.43 |41822.85 |
|2021-02-22T00:00:00.000Z|54207.32 |41192.25 |
|2021-02-21T00:00:00.000Z|57539.95 |40335.75 |
|2021-02-20T00:00:00.000Z|56099.52 |39534.15 |
|2021-02-19T00:00:00.000Z|55888.13 |38735.40 |
|2021-02-18T00:00:00.000Z|51679.80 |38036.50 |
|2021-02-17T00:00:00.000Z|52149.01 |37408.70 |
|2021-02-16T00:00:00.000Z|49199.87 |36864.40 |
|2021-02-15T00:00:00.000Z|47945.06 |36344.80 |
📍 **Exponential Weighted Moving Average (EWMA)**
```sql
DROP TABLE IF EXISTS base_table;
CREATE TEMP TABLE base_table AS
SELECT
market_date,
ROUND(close_price, 2) AS close_price,
ROUND(AVG(close_price) OVER w_1_to_14) AS sma_14,
ROW_NUMBER() OVER w_1_to_14 AS _row_number
FROM updated_daily_btc
WINDOW
w_1_to_14 AS (ORDER BY MARKET_DATE RANGE BETWEEN
'14 DAYS' PRECEDING AND '1 DAY' PRECEDING);
DROP TABLE IF EXISTS ewma_output;
CREATE TEMP TABLE ewma_output AS
WITH RECURSIVE output_table
(market_date, close_price, sma_14, ewma_14, _row_number)
AS (
-- The 1st query: we set initial output row using _row_number = 15
SELECT
market_date,
close_price,
sma_14,
sma_14 AS ewma_14,
_row_number
FROM base_table
WHERE _row_number = 15
UNION ALL
-- The 2nd query: we need to "shift" our output by 1 row
SELECT
base_table.market_date,
base_table.close_price,
base_table.sma_14,
-- Let's round out ewma_14 record to 2 decimal places
ROUND(
0.13 * base_table.sma_14 + (1 - 0.13) * output_table.ewma_14,
2
) AS ewma_14,
base_table._row_number
FROM output_table
INNER JOIN base_table
ON output_table._row_number + 1 = base_table._row_number
AND base_table._row_number > 15
)
SELECT * FROM output_table;
-- Pivot data for visualisation
SELECT
market_date,
'SMA' AS metric_name,
sma_14 AS metric_value
FROM ewma_output
UNION
SELECT
market_date,
'EWMA' AS metric_name,
ewma_14 AS metric_value
FROM ewma_output;
SELECT
market_date,
'Price' AS metric_name,
close_price AS metric_value
FROM ewma_output
ORDER BY market_date, metric_name;
```
**Illustration**
## ✨ Contribution
Contributions, issues, and feature requests are welcome!
To 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)**.
## 👏 Support
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© 2021 Leah Nguyen