It has been my observation that shorts often get covered much faster than longs get closed and the resulting volume drives prices back up very quickly. So, it seems to me that in a volatile market, shorts are affected more dramatically than long positions. Over the years I have observed that on most charts, down slopes seem steeper than up trends.
1. on optimizing, you always run the risk of getting into curve fitting, where you keep working at it until you see what you want. That is when the market will shift, and take your money. 2. the only way to get more of the trend is to see the future. Since that is not going to happen, you have to confirm the trend, then trade, thus, some will always be left. I prefer not to trade against the market. So, if a certain symbol begins to move in one direction, but the market is moving the other way, I am hesitant to make the trade. I want as much supporting my trade as I can get. ____________ Rik Rasmussen On Tue, Jun 22, 2010 at 7:31 AM, tzsuj <[email protected]> wrote: > > > Rik - why do you say the volatility has been rough on short positions ? I > would have thought it is choppy in both direction (albeit negative turns do > seem to be a bit steeper than the gains...). > > Also, a few questions with respect to my Best charts process: > > 1. Optimise frequently vs optimise once and hold until there is evidence > that the parameters are no longer working. At this stage, I would err on the > optimise frequently side, but I have read many posts which suggest the > opposite. What is your current thinking ? > > 2. Buy and sell signals - the paper test I just did used buy and sell > actions based on the individual stock's trend. So if the trend was bearish, > I sold and vice versa for buying. I did not restrict buy and sell actions to > the explicit signals in Best Charts (ie. I did not wait to see a 1.bullish > or bearish.1). Do you think this practice is sound (I guess I am only > catching part of the long or short action like this) ? If I was to use the > frequent optimisation method and I waited for the 1.bullish or bearish.1 > signals, then they may never appear due to the constantly changing > parameters. > > Thanks > > Paul > > > --- In [email protected] <BestCharts%40yahoogroups.com>, Henrik > Rasmussen <rikrasmus...@...> wrote: > > > > That is some nice work Paul. > > > > I am not surprised that shorter periods did well, given the short term > > volatility of the markets. > > > > I think the volatility has also been rough on short positions, except > maybe > > those short on BP stock. : ) > > > > > > _____________ > > Rik Rasmussen > > > > > > On Sat, Jun 19, 2010 at 9:28 AM, tzsuj <tz...@...> wrote: > > > > > Dear Forum > > > > > > I'm a relatively new user of Best Charts – haven't use it for real yet, > but > > > am trying to get a handle on it theoretically first. BC seems to be an > > > interesting and potentially powerful tool, but unfortunately it will > take a > > > bit of testing to get it set-up before I can use it with confidence.... > > > > > > I completed my first test run and I thought I would publish some of the > > > results – to get some discussion going on here and also feedback from > some > > > of the more advanced forum members. > > > > > > Some specifics of the test: > > > > > > Test Period: 19 April '10 to 17 May `10 > > > Test Stocks: Top 20 Australian stocks by market capitalisation (I used > > > these as they are large, well traded stocks – good volumes, and > hopefully > > > the market is well enough informed such that true technical analysis > > > principles can operate) > > > Test Indicator: BCI (I only did the BCI indicator as based on previous > > > optimisation testing, it seemed to give the best overall gains) > > > Parameters: BCI was `optimised' each day (according to the series > > > description below). I realise that the last parameter of the BCI is > user > > > defined and not automatically optimised – I just left this as the > default. > > > Buy/Sell Triggers: Instruction from the optimisation output each day – > > > simply go long when `Bullish' and go short when `Bearish' (as per the > intent > > > of the test series below) > > > Prices Used: Buy/sell at the opening price on the day following the > signal > > > generation. > > > Stop Losses: No stop loss triggers were used – investment was held > until > > > the signal changed. > > > Brokerage Costs: No brokerage costs were taken into account > > > > > > Test Series: #1 – Test Performance on Long Investment Only > > > Optimisation Period: 229 days > > > Optimise on Long Only > > > Invest Long Only > > > > > > Test Series: #2a – Test Performance on Long and Short Investment > > > Optimisation Period: 229 days > > > Optimise on Long & Short > > > Invest Long Only > > > > > > Test Series: #2b – Test Performance on Long and Short Investment > > > Optimisation Period: 229 days > > > Optimise on Long & Short > > > Invest Long & Short > > > > > > Test Series: #3a to 3h – Test Performance on Optimisation Period > > > Optimisation Period: 20, 60, 90, 120, 150, 180, 250, 320 days > respectively > > > Optimise on Long & Short > > > Invest Long & Short > > > > > > Issues that may have affected the results/market: > > > > > > The overall ASX market during the test period – commenced at the start > of a > > > downtrend, 3 weeks duration. Recovery in the last week. > > > The Greek sovereign debt crisis was rearing its head during the test > period > > > and got decidedly worse during the latter half of the test period. > > > The Australian government announced the imposition of a higher rate of > > > taxation on mining companies during the last week of the test period – > this > > > had a severe negative effect on resource stocks and resource service > > > industry type stocks. > > > > > > > > > Test results: > > > 1. Interestingly, the percentage of winning trades for each test series > > > (so # of winning trades for all 20 stocks in each test series compared > to > > > the total # of trades for all 20 stocks in each test series) came in at > > > around 50% - the law of averages...... series #1 was low @ 38%, but the > rest > > > ranged from high 40's to mid 50's. This would suggest to me that using > BCI > > > in the way that I did is no better than flipping a coin – 50% of the > time > > > you will get heads and 50% of the time you will get tails. This is a > bit > > > disappointing as I would view the ability to pick a winning trade as > one of > > > the most important metrics – you need to be on winning trades for > something > > > like 70% of the time to have some sort of confidence that you are on > the > > > right track. > > > > > > 2. In terms of the most number of winning trades, tests 3b (60 day), 3c > > > (90 day) and 3e (150 day) were the best performers with winning rates > of > > > 57%, 54% and 59% respectively. Encouraging, but not stellar. > > > > > > 3. 2/20 stocks did extremely well in terms of the # of test series with > > > winning trades <70% - RIO (6/11 test series) and WOW (8/11 test series) > – > > > one resource stock and a consumer staple stock. During the test period, > RIO > > > was in a major down trend and WOW was in a range-bound pattern. As a > > > comparison other stocks in the test population of 20 came in at 2-3 (at > > > best) with series trades >70% winners. I don't know if there is > anything to > > > be read from this observation...... > > > > > > 4. % winning LONG trades (41-67%) was decidedly greater than the % > winning > > > SHORT trades (29-36%) for all test series. This is interesting, but not > > > sure what I can conclude from it. It seems to suggest that BCI's > shorting > > > accuracy is less than that of long trigger accuracy. If you were to > extend > > > this concept and say, only invest long (but optimise on long and > short), you > > > would come up with a better outcome, but you would still only get in > the > > > high 60%'s for # of winning trades – close but not quite at the comfort > > > level. WOW performed very well in terms of % of long winning trades – > 7/11 > > > tests achieved >70% winners. > > > > > > 5. It is difficult to present statistically correct and meaningful data > > > for this item, but in general, the % gain/trade for winning shorts was > > > significantly greater compared to the gain/trade for winning long > trades. % > > > Losses/trade for losing longs and losing shorts were about equal. > > > > > > 6. Performance against benchmarks – 4 benchmarks were used - #1 was the > > > `buy and hold' movement of the share price for each stock during the > test > > > period, #2 was a theoretical 7% pa return, #3 was the movement of the > ASX > > > 200 index (top 200 ASX stocks) over the test period, and #4 > preservation of > > > invested capital. > > > 6a. Stock buy and hold - All test series performed considerably better > > > than the buy and hold scenario for each stock – the BCI series achieved > > > gains > the benchmark for between 70-90% of the 20 stock test set. > > > 6b. Nominal 7%pa interest – Mixed results. Series 1 and 2a were > > > decidedly worse than the benchmark (10-15% of stocks outperformed the > > > benchmark) – this is logical as these were series without shorting (so > less > > > time in the market compared to a constant 7% earning rate). Other > series > > > ranged from 40-70%, with best performers being 3b (70%), 3c (65%) and > 3e > > > (65%). The remainder averaged out at the 50% mark......the law of > averages > > > again ?? > > > 6c. ASX200 buy and hold – similar to 6a – not surprising given make-up > > > of ASX200 index. > > > 6d. Preservation of capital – initial investment capital was preserved > > > in most series (generally 60-70% of the 20 stocks preserved capital, > for > > > each of the test series), with the exception of series 1 and 2a (only > 20% of > > > the stocks preserved capital). The inability to short in these series > is > > > the obvious difference to the other series, however this doesn't > explain the > > > poor performance – if you couldn't short, then you just keep the money > > > uninvented – so no risk. The only reason I can think of here is that > the > > > falling market did not correlate well with the 229 day test period data > and > > > the long only optimisation condition (for test 1) and the mismatched > > > optimisation (long and short) vs investment practice (long only) (for > test > > > 2a) resulted in BCI not predicting well. > > > > > > 7. Annualised % returns across the 20 stock spread – if you invested > equal > > > initial amounts across the 20 stocks for each of the series, the > following > > > annualised returns are realised: > > > > > > Series 1 -38% pa > > > Series 2a -41% pa > > > Series 2b -1% pa > > > Series 3a 22% pa > > > Series 3b 47% pa > > > Series 3c 44% pa > > > Series 3d -26% pa > > > Series 3e 20% pa > > > Series 3f 24% pa > > > Series 3g -13% pa > > > Series 3h 3% pa > > > Benchmark 1 -87% pa > > > Benchmark 2 7% pa > > > Benchmark 3 -94% pa > > > Benchmark 4 0% pa > > > > > > > > > So, in summary, the BCI testing showed OK results against a `buy and > hold' > > > strategy in a falling market, but the specific process used did not > deliver > > > consistently high probability results (refer point 1). Shorter > optimisation > > > periods (60 and 90 day) seem to work the best in this instance. Long > > > trading was more successful than shorting (don't know why – this is > counter > > > market trend) and shorting wins were bigger than long wins. No results > came > > > close the optimised theoretical backtest results of ++100%pa (not sure > if > > > this is realistic though). > > > > > > I will conduct more testing going forward. > > > > > > Any comments or suggestions. Analysis spreadsheet is available upon > > > request (it is a bit messy, but you will get the idea). > > > > > > > > > Paul > > > > > > > > > > > > > > > ------------------------------------ > > > > > > Yahoo! Groups Links > > > > > > > > > > > > > > > > >
