2017年8月1日 星期二

20170801 又係盈富 - 沽清@28.1

今日盈富基金(2800) 52週高 波幅 27.95 - 28.25
因已到系統賣出價位,所以沽清了退而休則股組合內的盈富@28.1。

不經不覺買賣盈富基金已3年。自2014年9月高位買入,經歷了2015大時代,2016小時代,溝淡至2017年初的10000股@約$23.7。之後從$24逐步減持,今日終於沽清!
埋單計數:投入                    收益

485,232.80         +20,279.78   (+4.18%)



成績認真麻麻!

同期我太太的穩陣師奶組合內的盈富因少買賣,表現良好:
                     278,964.00         +58,617.00    (+21.01%)


2017年7月18日 星期二

20170718 盈富基金的威力

自從用了www.ticker.com.hk來計算股票組合的回報之後,發覺我的「退而休則股」組合回報
資金加權平均收益率
 (2017 年 (YTD) +15.93% 比我太太的「穩陣師奶」組合 (+21.80%) 大幅落後。原因可能是我半年來放了大部份盈富而換入了香港小輪(0050),維他奶(0345),置富(0778) 和粵海投資(0270)。

現時我的「退而休則股」組合總回報(5.88%)。我嘗試模擬如果我唔放盈富,效果是:(8.05%)

最後我用程式回測(backtest)了以下的情景:假設在2012年9月6日用20萬買入10000股盈富@$19.7。若持有(buy & hold) 5年到今日(2017-07-18),年化回報率(CAGR)是5%,收益是53%,大約現值30萬6千(包括股息)。若用低買高賣策略(5年來買7次賣5次,CAGR只不過是加了少少到5.6%,收益60%,現值32萬,辛苦吧!

2017年7月13日 星期四

20170713 代太太減持小量盈富(2800.hk) @27.00

今天盈富基金(2800.hk) 52週高
原本昨晚收市是$26.7。我太太告訴我代她放小量盈富@$26.85。誰知今早開市恆指大升300多點,竟然成交價是$27.00!有小小驚喜!

我的組合內的盈富因要增加現金比例,自從恆指25000點時已在$24.85、$25.2、$26.1放了大部份了。但我太太因沒有心理負擔,不會時常買賣,反而回報比我高很多!

2017年7月10日 星期一

20170710 沽清匯豐碎股,和增持粵海投資(0270.hk) @10.3

今天決定沽清匯豐碎股,原來碎股只能用即市價賣走,而賣價@73.5比市價74.15低10多個價位。今天匯豐收報74.6,52週高,雖然賣得比較低,但因為碎股相差幾十元而已,無所謂啦!5年埋單蝕-19,060.64 (-20.36%) 

另外不知為什麼粵海投資(0270.hk)又跌破系統低價,所以增持了@10.3。這個買入決定不知對定錯,因為現在是牛市,應該增加現金比例才是。

2017年7月6日 星期四

20170706 增持維他奶(0345.hk) @15.54

今天維他奶(0345.hk)不知為什麼股價下跌到了我的買入價
維他奶國際1月低,所以根據系統增持了@15.54。維他奶一直以來是我的組合裡賺錢的股份,雖然它的息率(2.26%)很低,但是它的增長很高,roe又高,可用來平衡組合裡的高息公用股和reit。

2017年7月1日 星期六

20170701 Kelly Criterion 在股票的應用

一直以來有留意Kelly Criterion 在股票的應用。在看過池兄的blog文後http://poolshunter.blogspot.hk/2017/06/kelly-criterion.html ,便在網上找尋"simultaneous kelly for stock portfolio",找到一些更豐富的內容,現在綜合一下,請各位指教。

1. How to apply Kelly criterion to a portfolio made by a stock plus a option?
https://quant.stackexchange.com/questions/26324/how-to-apply-kelly-criterion-to-a-portfolio-made-by-a-stock-plus-a-option

I know that by a portfolio made by only by one stock (and a risk free bond) I can use the formula:

f* = (R-Rf)/d^2

f* - Wealth fraction that maximize the log return
R - Asset Return
Rf - Risk-free return d - Standard Deviation

Answer:
======
Kelly is mostly based upon assets with zero correlation made independent of each other.
The way I approximate Kelly for multiple bets with correlation is:

Assume after your first bet the capital is gone.
Place a second bet based upon the Kelly of the remaining capital.
Factor in correlation..
Part 3 is the challenging part. I assume that with multiple bets at zero correlation placed simultaneously that I would bet the full Kelly per bet made. I assume that with multiple bets at a correlation of 1, I would divide the Kelly by the number of bets. So if for example I were to make 5 bets with a Kelly of 20%...
a correlation of 1 would be 20% divided by 5 or 4% per bet. A correlation of zero would be 1-(0.80^5)
to determine total capital at risk and then divide by 5 which is ~13.45% per bet.
A correlation of 50% is the average of the two or ~8.7% Anything else is a weighted average
but you have to be careful not to get the weightings backwards.
For example a correlation of 20% you take 80% of the Kelly amount 13.45 and 20% of 4% and sum them together.

小記:看不懂Part 3

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2.

Quantitative Trading: Kelly vs. Markowitz Portfolio Optimization

In my book, I described a very simple and elegant formula for determining the optimal asset allocation among N assets:

F=C-1*M   (1)

where F is a Nx1 vector indicating the fraction of the equity to be allocated to each asset, C is the covariance matrix, and M is the mean vector for the excess returns of these assets. Note that these "assets" can in fact be "trading strategies" or "portfolios" themselves. If these are in fact real assets that incur a carry (financing) cost, then excess returns are returns minus the risk-free rate.
小記:這就是終極版的「發達公式」,非我等凡人可用+-*/或excel計算出來的!
------------------------------------ 
3. Simple closed form solution for unconstrained Simultaneous bet Kelly staking
So given, for example, events A, B, C, D, and E, with corresponding single-bet Kelly stakes of κA, κB, κC, κD, and κE,
then the Kelly stake for the 1-team parlay consisting of only bet A would be:
κA * (1-κB) * (1-κC) * (1-κD) * (1-κE)
While the Kelly stake for the 3-team parlay consisting of bets A, B, and C would be:
κA * κB * κC * (1-κD) * (1-κE)
Much simpler, no?
小記:這個可用!已改了我的組合程式上試試!

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4. Algorithms for optimal allocation of bets on many simultaneous events
Chris Whitrow 

Conclusions
When the number of bets is small, the optimal sizes of bet seem to be almost exactly proportional to the Kelly stakes on individual bets. 

小記:這個最簡單!已改了我的組合程式上試試! 

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(註:我的組合程式上的個別kelly是用以下的公式的:

Sharpe ratio S = (R-Rf)/d,
f = (R-Rf)/d^2 = S/d

據說the maximum compounded growth rate g is given by g=r+S^2/2. 
We usually drop the risk-free rate, so we have g=S^2/2.

現時盈富(2800.hk)的sharpe ratio大約是0.57


2017年6月30日 星期五

20170630 匯豐放剩碎股@$74

一直以來想清倉匯豐,因為四年以來平均買入價$77,在2016年7月尾因忍不住放了@$50.9,蝕了2萬幾元,十分心痛!今天終於放剩碎股@$74。埋單蝕了(-20.71%)