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    Цитата Сообщение от avtomat Посмотреть сообщение
    какие откровения о стопах...
    :D
    ...не завидуй... :D
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    Количественно или качественно? На первом варианте ощущения более комфортные нежели на втором,который порой вызывает наверно у меня чувство шлюхи,которая позавидовала проститутке.
    а брокер выходит в роли сутенера выступает, ну и ему фиолетово кого и как:D
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    Цитата Сообщение от avtomat Посмотреть сообщение
    какие откровения о стопах...
    :D

    да уж тут не скучно :smartass:.
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    Цитата Сообщение от paukas Посмотреть сообщение
    Разумеется. Некоторые даже математике не доверяют. Сказали им - хвосты должны быть длинные. Верят на слово. :)
    Ладно, в конце концов не суть важно, любые модели и представления ограничены и не отражают в полной мере суть явления. Главное, чтобы прибыль была, а там хоть по книге перемен сигналы получай - лишь бы не в убыток. Мыслишки свои впрочем накидаю в своей ветке как будет время.
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    Цитата Сообщение от ДоЦент Посмотреть сообщение
    Ладно, в конце концов не суть важно, любые модели и представления ограничены и не отражают в полной мере суть явления. Главное, чтобы прибыль была, а там хоть по книге перемен сигналы получай - лишь бы не в убыток. Мыслишки свои впрочем накидаю в своей ветке как будет время.
    Я про то что всё надо проверять, тогда хвостов не будет :)
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    Цитата Сообщение от paukas Посмотреть сообщение
    Я про то что всё надо проверять, тогда хвостов не будет :)
    Проверка - дело хорошее, только что она нам покажет. Что за последний год, два... пять, система принесла бы нам столько то процентов годовых. А в следующем рынок поменяет свои параметры будет вместо тренда сплошной флет к примеру и полетят все расчеты к черту.
    Когда имеешь дело с материальным миром проверка и эксперимент хороши. Измерил к примеру твердость и скорее всего аналогичный слиток той же стали будет иметь близкую к ней твердость - можно на это рассчитывать.
    А тут как говорил великий экономист Леонтьев кажется: ни в одной другой области не используется такая изощренная математика со столь слабой отдачей от этого как в экономике.
    Сам изучаемый предмет - коллективная психологогия при производстве, распределении и потреблении материальных благ - сложнее, изменчивее, чем любая железка и субъективен по сути.
    Даже на защите пришлось поспорить с автоматчиками. Неправомерно переносить передаточные функции и обратные связи один в один с машин на экономические объекты. Это разве что в сталинской модели экономики эффективно сработает.
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    Проверка - дело хорошее, только что она нам покажет. Что за последний год, два... пять, система принесла бы нам столько то процентов годовых. А в следующем рынок поменяет свои параметры будет вместо тренда сплошной флет к примеру и полетят все расчеты к черту.
    .
    Ага, марсианы прилетят, захватют землю и будут на рынке торговать.
    Вот тогда рынок поменяет свои параметры. Или коммунисты придут к власти в америке и установят твёрдый курс социалистического доллара к буржуазному евро.
    Бросьте, рынок не меняется последние 300 лет. С чего ему вдруг меняться то?
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    Stop Losses – Help or Hindrance?

    Introduction

    Many traders and investors place Stop Loss orders as part of their day-to-day investment activity. Virtually all trading books recommend the use of stops, with many making statements like "Trading without stops is like driving without a seatbelt". The argument for the use of stop-loss rules seems inherently sound, yet there appears to be no real evidence that stops are providing the safety benefits that many traders expect.

    With regard to medium to longer term equity trading systems (which appears to cover the majority of investors and traders), it may well be that stops are causing more harm than good!

    As traders, we are used to having an initial stop loss on a trade, and congratulating ourselves when the stop saves us money as the trade goes south very quickly. Although a stop-loss rule may save us from damage on specific trades, it seems doubtful whether this beneficial effect actually holds when we measure it at a portfolio level. There are a number of specific reasons why this may be the case, which I will touch on later in this series.

    As traders, we shouldn't really focus on the return of each individual trade; rather we should focus on the overall return of our portfolio. A large amount of my empirical testing appears to show a mismatch between stop performance at an individual trade level, and stop performance at a portfolio level.

    In this series of articles, I would like to demonstrate the mismatch that stops appear to introduce, and show you a way to be able to test this for yourself. This article is part 1 of a 3-part series. In this article, I will introduce an example system, and demonstrate how to benchmark the system with and without a variety of stops, and statistically analyse the results.

    You can then use this same process to benchmark the effect stops are having on your own individual trading system, to determine if you are actually benefiting from using stops.

    Measuring the impact of Stops

    To measure the impact of stops on a trading system, it is necessary to consider the effect that stops have on both individual trades, and on specific portfolios constructed from those trades.

    To assess the effect that stops have on individual trades, we can benchmark and measure changes in:
    Trade daily mean return ($) – average return per day
    Average number of days trades are open

    To benchmark the raw trades signalled by the entry and exit rules, we initially assume unlimited equity, and a nominal investment of $10,000 per trade.

    To assess the effect that stops have on specific portfolios, we can benchmark and measure changes in:
    APR% (Annual Percentage Return) – a portfolio's return
    Max DD% (Maximum % Drawdown) – which shows the worst case drawdown (peak to valley) that the portfolio equity curve has suffered.
    Sharpe Ratio - which shows the amount of risk taken per unit of return. Ignoring the risk-free rate adjustment, the Sharpe Ratio is a measure of how volatile portfolio returns have been. (As an example, two different traders may both have achieved a return of 20% over time. The Sharpe Ratio will be highest for the trader who has achieved this result with the least volatility.)

    When benchmarking a portfolio, it is important to take account of the amount of equity used. In this case, a relatively simple 'percentage of equity' model is used. We allocate 2% of available equity to each trade, from an initial starting capital of $1,000,000.

    By monitoring the variables above, we can benchmark the metrics that are obtained from a set of trading rules. We can then add stops to the trading rules and repeat this process. This will allow us to empirically measure the effects that the stops have on those key metrics. We can then statistically determine whether the portfolio outcome has been improved by the addition of the stop rules.

    Case Study

    The majority of traders would be best described as medium to longer-term equity investors. In essence, this means that they trade ordinary shares, and aim to hold each share from several months to several years. Typically, this group of investors name themselves 'trend traders', and their aim is to identify and ride a trend for as long as possible. Often one or more simple (or exponential) moving averages provide entry and exit setups. Typically, this group also only trades the long side.

    For this reason, I have chosen a 60-day ema crossover system as the example case study system . A 60-day ema crossover system buys when the price crosses above a 60-day ema, and sells when the price crosses below a 60-day ema.

    An example trade is shown below in Figure 1. The pink line represents the value of the EMA(60)


    The data chosen for the case study is the constituents of the ASX200 (since inception April 2000) until the end of 2009. Where possible, I have adjusted this data for delistings and code changes, and trading results include an allowance for transaction costs. To address survivorship bias, buy signals are only issued on stocks which were constituents of the ASX200 on the day the signal was generated.

    Remember the objective is not to determine whether these are desirable rules for trading; it is to allow us to select and emulate the basic characteristics of the kind of stocks that the majority of traders and investors in the ASX200 are focused on.
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    No stops

    Initially, we need to benchmark the buy and sell rules without any stops. This gives us a baseline against which to compare the performance of the stops we will introduce.

    Raw Trades

    The key characteristics of the raw trades generated by buying/selling $10,000 worth of stock every time the buy/sell conditions occur are:

    Daily Mean Return = $ 0.61, Average Number of days trades are open = 21.44

    Later, when we introduce a variety of stop combinations to the buy/sell rules, we can measure the effects they have using this baseline.

    Portfolio

    The key characteristics of the portfolio generated by these trades are:

    APR = 2.63 %, MAX DD = -34.63 %, Sharpe Ratio = 0.31

    Now we know how much potential return there is in the rules (APR%), how risky those rules are (DD%), and a measure of the overall risk for that specific return (Sharpe ratio). Later, when we introduce a variety of stop combinations to the buy/sell rules, we can measure the effects they have using this baseline.

    Initial Percentage Stops

    Many traders simply use a fixed percentage to determine their stop level price. As an example, a trader might say, "I will set a stop loss 5% below my entry price". Here, we test every initial stop loss percentage threshold from 1% - 10% in steps of 1, for all the trades generated by the ema crossover rules.

    The impact that these initial stops have on both return and risk is presented next.
    Raw Trades


    From the table presented, it is clear that none of the stop methods tested improved the 'NO STOP LOSS' portfolio's daily mean return. This is as expected, given that, by definition, an initial stop loss rule entails selling at a loss. To determine whether this approach has decreased our risk, we next test within a portfolio setting.
    Portfolio


    From this table, we can see that none of the stop methods have improved the 'NO STOP LOSS' portfolio's APR. Further, none of the stop loss settings was able to improve the Sharpe Ratio. Some of the higher percentage stops achieve similar Maximum Drawdown%, but none of the stop loss settings was able to improve the Sharpe Ratio. In essence, all combinations of stop loss tested achieved less return, and were riskier.

    Implications

    To statistically compare the portfolio results, we can use the ANOVA procedure, which allows us to simultaneously compare all the trades generated under the 'NO STOP LOSS' condition, with all the sets of trade possibilities from the 10 stop loss combinations. This allows us to determine whether there is any statistical significance in our findings.

    The results indicate that no benefit has been obtained from any of the stop combinations. I have purposefully omitted a detailed explanation of using the ANOVA procedure in this article, to allow us to keep focused on the effects of stop losses. Those readers that are interested in pursuing the benchmarking of trading systems using statistical methods can find details of this and many other useful procedures in my book, Designing Stockmarket Trading Systems (with and without soft computing).

    Summary

    In this article, I have benchmarked the results of a simple EMA crossover strategy. Next, the strategy was tested with a variety of initial percentage based stops to see if adding these stops was able to decrease the risk in the strategy. It was found that all stops tested increased the risk and reduced the return of the original strategy.

    In the next article, I will test percentage-based trailing stops and ATR-based trailing stops to see whether these types of stops can decrease the strategy risk.
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    а что случилось? по-русски об этом уже нельзя?

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