Rainbow 6 Siege - Cold Zero - 100% Health Finish

Rainbow 6 Siege - Tubular Assault - 100% Health Finish

Brokerage fee comparison at a glance - Hong Kong

			Trading Value and brokage fee in HKD
Broker	Minimum	Rate	5,000	10,000	100,000
Bank of China (Gold Star)	100	0.200%	100.00	100.00	200.00
Bank of East Asia Supreme Gold	80	0.250%	80.00	80.00	250.00
Bright Smart	50	0.067%	50.00	50.00	66.80
China Construction Bank	0	0.180%	9.00	18.00	180.00
Citigold	100	0.200%	100.00	100.00	200.00
DBS Treasures	0	0.250%	12.50	25.00	250.00
Fubon Bank	80	0.250%	80.00	80.00	250.00
Hang Seng Bank	100	0.250%	100.00	100.00	250.00
HSBC	100	0.250%	100.00	100.00	250.00
Interactive Brokers	18	0.080%	18.00	18.00	80.00
SHK direct	88	0.138%	88.00	88.00	138.00
Standard Chartered Bank	0	0.200%	10.00	20.00	200.00

Winner			China Construction Bank	China Construction Bank & Interactive Brokers	Bright Smart
Fee			9.00	18.00	66.80

Additional cost

– SFC Transaction Levy 0.0027% of the transaction amount

– Stock Exchange Trading Fee 0.005% of the transaction amount

- Stamp Duty 0.1% of the transaction amount

Date: 1/27/2018

Live document: https://docs.google.com/spreadsheets/d/e/2PACX-1vRgaNDCI4tByBYyxVM7CZzSv0psCb2ZXRF-umj_mgPX0rAD_jnzy-7CXfMl2MpAPw1T6_cw--rwFyNC/pubhtml?gid=0&single=true

Listing all tables in a database and their row counts and sizes

CREATE TABLE #RowCountsAndSizes (TableName NVARCHAR(128),rows CHAR(11),     
       reserved VARCHAR(18),data VARCHAR(18),index_size VARCHAR(18),
       unused VARCHAR(18))

EXEC       sp_MSForEachTable 'INSERT INTO #RowCountsAndSizes EXEC sp_spaceused ''?'' '

SELECT     TableName,CONVERT(bigint,rows) AS NumberOfRows,
           CONVERT(bigint,left(reserved,len(reserved)-3)) AS SizeinKB
FROM       #RowCountsAndSizes
ORDER BY   NumberOfRows DESC,SizeinKB DESC,TableName

DROP TABLE #RowCountsAndSizes

Source: https://www.sqlmatters.com/Articles/Listing%20all%20tables%20in%20a%20database%20and%20their%20row%20counts%20and%20sizes.aspx

Engine part names

https://i.pinimg.com/originals/61/3f/69/613f6997b566882fca77970717899380.jpg

How to find text in entire workbook

Your eyes are not built for searching text in a boring excel file.

To protect your eyes, you have to know what the computer can do for you.

Make good use of the "find and replace" can save your eyes from bleeding.

Stock price predication using Weka

Background

As a seasonal stock trader, it has been a dream for me to have a stock info system suggesting order price.
Traditional technical indicators do help me a lot for giving buy / sell signal most of the time, but would big data with applying machine learning techniques help me to capture every trading opportunities? 

During the Easter holiday, I spent a few days for trying out using WEKA for stock price prediction.

Objective

To predicate day low and day high of the next trading day.


Choosing stock - 0002 CLP Power Hong Kong

Stock market: Hong Kong 
Stock code: 0002
Stock name: CLP Power Hong Kong Limited

The CLP Group (Chinese: 中電集團) and its holding company, CLP Holdings Ltd (SEHK: 0002) (Chinese: 中電控股有限公司), is a Hong Kong electric company that has businesses in a number of Asian markets and Australia. It is one of the two main electric power generation companies in Hong Kong.

There are couple of reasons to pick CLP Power 

1. Sizable market cap
2. Sizable trade volume per trading day
3. Stable income
4. Stable customer base (monopoly, the only electric power company in Kowloon and New Terror ties, covering over 85% of total Hong Kong population)
5. Stable dividend scheme
6. Stable government policy, less sensitive to political and financial issues

Feeding Weka- Preparing data files

I used Yahoo Finance as the data source for 4/Jan/2000 - 4/May/2017 (4504 records), and later normalize the data and adding technical indicators. 

Be aware, Weka works with file in ARFF only. Read http://www.cs.waikato.ac.nz/ml/weka/arff.html for more.

WEKA comes with a ARFFViewer to read ARFF.

Data files:

1. Yahoo raw data file
2. Normalised data with technical indicators
3. Normalised data in ARFF data format
4. Training set (less records in test set) in ARFF data format
5. Test set (last 100 records)

Engine On

Open the "Explorer"

Press "Open File"

I pick 0002-COMPLETE.arff to predict change% of high on 5 May 2017.

In my dataset, each record comes with two expecting results. 

1. The change percentage of lowest price; 
2. The change percentage of the highest price. 

Either one has to be removed for processing. 

i.e. If you wanted to predict the change of the highest price, remove the change of lowest price.

Go to "Classify" and choose "AutoWEKAClassifier" as classifier

I gave 15 minutes to Weka to predict the change of highest price.

Results

I got the result after the coffee break. Weka found the best classifier.

And I scrolled down, there were the summary and the prediction result. The accuracy 
seemed to be very disappointing. But when I looked deeper into the results, it might not be that bad as it seems. 

Result files

1. Low
2. High

I decided to put the prediction results into a trading simulation to see how it looks. It is the end of part 1.