DESCRIPTION:

Implements technical analysis functions as MySQL UDFs.

Currently implemented functions are:

TA_SMA
TA_SSMA
TA_EMA
TA_RSI
TA_TR (True Range)
TA_SUM (Running sum, as opposed to aggregate sum provided by mysql)
TA_STDDEVP (Running stddevp, as opposed to aggregate stddevp provided by mysql)
TA_PREVIOUS

Other indicators which can be derived from those functions include:

MACD
Bollinger Bands
ADX
ATR

AVAILABILITY:

Source repository at: http://github.com/joaocosta/lib_mysqludf_ta

DOCKER IMAGES

# Create a new empty database
docker run --name fxdata -e MYSQL_ROOT_PASSWORD=root -e MYSQL_DATABASE=fxdata -d mysqludf/ta

# Alternatively, mount an existing database outside the container
docker run -v /my/databasedir:/var/lib/mysql --name fxdata -d mysqludf/ta

# Connect with mysql client inside a separate container
docker run -it --link fxdata:mysql --rm mariadb sh -c 'exec mysql -h"$MYSQL_PORT_3306_TCP_ADDR" -P"$MYSQL_PORT_3306_TCP_PORT" -uroot -p"$MYSQL_ENV_MYSQL_ROOT_PASSWORD"'

See more examples of how to use the mariadb image at https://hub.docker.com/_/mariadb/

COMPILING:

Windows:

A binary dll is shipped with this release.

Copy it to the MySQL plugin directory. To find out what the MySQL plugin directory is run this in a MySQL prompt:

show variables like 'plugin_dir';

The following article describes how to compile MySQL UDFs in Windows: http://rpbouman.blogspot.com/2007/09/creating-mysql-udfs-with-microsoft.html

Linux:

./configure
make
sudo make install

If this fails, run ./autogen.sh and repeat the "configure && make && make install" sequence

For more information on compiling MySQL UDFs:

http://dev.mysql.com/doc/refman/5.1/en/udf-compiling.html
http://dev.mysql.com/doc/refman/5.1/en/adding-udf.html

#Install the UDFs

From the MySQL prompt:

Windows

CREATE FUNCTION ta_ema RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_max RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_min RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_previous RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_rsi RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_sma RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_ssma RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_stddevp RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_sum RETURNS REAL SONAME 'lib_mysqludf_ta.dll';
CREATE FUNCTION ta_tr RETURNS REAL SONAME 'lib_mysqludf_ta.dll';

Linux

CREATE FUNCTION ta_ema RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_max RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_min RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_previous RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_rsi RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_sma RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_ssma RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_sum RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_stddevp RETURNS REAL SONAME 'lib_mysqludf_ta.so';
CREATE FUNCTION ta_tr RETURNS REAL SONAME 'lib_mysqludf_ta.so';

Or simply:

cat setup/*_up.sql | mysql -uroot

Test your installation

make check  #This assumes a local mysql instance is available, username root with no password

FUNCTIONS:

To try the examples below import the provided sampledb.sql into a MySQL database.

mysqladmin -u root create lib_ta
mysql -u root lib_ta < sampledb.sql
mysql -u root lib_ta

---

ta_ema - Exponential moving average

ta_ema(
    float data, 
    int period
)

• data - The data to average
• period - Running period to calculate for

Example:

To calculate a 50 period EMA of closing prices:

SELECT datetime, ta_ema(close, 50)
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_sma - Simple Moving Average ( aka Running average )

ta_sma(
    float data, 
    int period
)

• data - The data to average
• period - Running period to calculate for

Example:

To calculate a 50 period SMA of closing prices:

SELECT datetime, ta_sma(close, 50)
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_ssma - Smoothed Moving Average ( aka Running average )

A smoothed moving average, as used to calculate ATR and RSI. See also: https://en.wikipedia.org/wiki/Moving_average#Modified_moving_average

ta_ssma(
    float data, 
    int period
)

• data - The data to average
• period - Running period to calculate for

Example:

To calculate a 50 period SMA of closing prices:

SELECT datetime, ta_ssma(ta_tr(high, low, close), 14)
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_rsi - Relative Strength Index

ta_rsi(
    float data, 
    int period
)

• data - The data to calculate rsi for
• period - Running period to calculate for

Example:

To calculate a 14 period RSI of closing prices:

SELECT datetime, ta_rsi(close, 14)
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

To calculate a 10 period ta_ema of ta_rsi(14):

SELECT datetime, ta_ema(ta_rsi(close, 14), 10)
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_stddevp - Running Standard Deviation

ta_stddevp(
    float data, 
    int period
)

• data - The data to calculate on
• period - Running period to calculate for

Example:

This function is useful for calculating indicators such as Bollinger Bands.

To calculate the upper limit Bollinger Band of 2 standard deviations over a 21 period sma:

SELECT datetime, ( ta_sma(close,21) + 2*ta_stddevp(close,21) ) AS `BOL_UP`
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

To calculate the lower limit Bollinger Band of 2 standard deviations over a 21 period sma:

SELECT datetime, ( ta_sma(close,21) - 2*ta_stddevp(close,21) ) AS `BOL_DOWN`
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_sum - Running Sum ( as opposed to aggregate sum )

ta_sum(
    float data, 
    int period
)

• data - The data to average
• period - Running period to calculate for

Example:

To calculate a 50 running sum of closing prices:

SELECT datetime, ta_sum(close, 50)
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_tr - Calculate True Range

ta_tr(
    float high,
    float low,
    float close
)

• high - The Highest price for the period
• low - The Lowest price for the period
• close - The Closing price of the period

Example:

To calculate True Range

SELECT datetime, ta_tr(high, low, close)
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_previous - Calculate True Range

ta_previous(
    float data,
    int period
)

• data - The data to lookback into
• period - Number of periods to look back into

Example:

See if today's close is greater than yesterday's close SELECT datetime, close > ta_previous(close,1) FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_max - Running Max

ta_max(
    float data,
    int period
)

• data - The data to search the maximum for
• period - Running period to calculate for

Example:

To return the maximum close over the last 50 periods: SELECT datetime, ta_max(close, 50) FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

ta_min - Running Min

ta_min(
    float data,
    int period
)

• data - The data to search the minimum for
• period - Running period to calculate for

Example:

To return the minimum close over the last 50 periods: SELECT datetime, ta_min(close, 50) FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

macd - Moving Average Convergence / Divergence

MACD is defined as the difference between two emas

SELECT datetime, ta_ema(close,12) - ta_ema(close,26) AS `MACD`
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

The MACD signal line is defined as an EMA of MACD

SELECT datetime, ta_ema(ta_ema(close,12) - ta_ema(close,26), 9) AS `MACD_SIGNAL`
FROM ( SELECT * FROM EURUSD_86400 ORDER BY datetime ASC LIMIT 1000000 ) AS T;

---

USING WITH INTEGER DATA INSTEAD OF FLOATS:

Financial data tends to be stored as floats, however sometimes it might be useful to run these functions with integer data. This can be achieved using MySQLs CAST:

SELECT ta_ema(CAST(integer_data_field AS DECIMAL(65), 14) FROM TABLE;

CREATING NEW FUNCTIONS:

• The easiest way would be to copy one of the existing .c files to a different name and modify its implementation.
• Edit test/03_test.sh and add a suitable test to validate results of the new function using the provided dataset.
• Add a setup script for your new function under the setup directory. These are used by automated deployment mechanisms.
• Edit Makefile.am and add the new source file.
• To generate a new configure script, run the provided autogen.sh ( this depends on automake, autoconf and libtool being installed ).

CONTACT:

João Costa [email protected]