drawdown Archives - アルゴリズムと機械の外国為替戦略

2 Painful hits

2 月 13, 2017 によってショーンオバートン 14 コメント

December and January were extremely unkind to me. I took a huge loss on 12 月 9 that coincided with the Fed meeting and another big punch in January. In total, I went from a 28% profit to a ~4% net loss.

Deservedly, my inbox quickly flooded with comments and suggestions on the drawdown. The most common of those was to stop trading during news events.

だから… why am I still trading during news events? There are a few answers to that question.

カーブフィッティング

It’s not like the strategy loses money on every single news event. それは、します。 100% true that news events like the Fed meeting can and badly hurt. Say that I’m determined to exclude news events in the future. I’d have to

Collect historical news event data
Create a second algorithm, which selects the news events that forbid and allow trading to continue
Test how the news algorithm interacts with Dominari
Repeat this many times until I’m happy with the final result

Due to the tiny number of news events that impact the markets like the December 9th announcement, my data set is miniature. The risk of overfitting to historical news events is huge.

Working with tiny amounts of data provides little in the way of long run confidence. Focusing my efforts elsewhere is far more likely to improve performance and requires much less work.

Too many trades

Too many trades sounds a bit naive, so let’s dig into what that means. Dominari trades a portfolio of 7 異なる機器. All instruments cross with USD.

ユーロドル
GBPUSD
USDCHF
AUDUSD
NZDUSD
USDJPY
USDCAD

Many subscribers correctly observed that the major losses occurred with trades open on all 7 pairs in the portfolio at the same time. A good predictor of trade performance is the number of trades open simultaneously.

1-3 trades seems to be consistently profitable
4-5 trades leads to biting my nails
6-7 trades is neutral to disastrous

Testing and confirming the max open trades rule was quick and easy. 5+ trades is very dangerous.

Accordingly, Dominari now exits all open trades if there are 5 or more trades open at any given time.

The next feature of Dominari will be a reversal strategy. Dominari was clearly prone to sudden equity changes if 5+ trades were open at the same time.

Make the losses work for us

An obvious counter strategy is to open trades in the opposite direction whenever Dominari would otherwise open too many trades. Testing the idea is very easy.

Coding a Dominari reversal strategy, しかし, would require a major reprogramming of the expert advisor’s code.

The number of trades per year would be miniscule. I doubt that it would average even 1 trade per month.

The idea is that Dominari can be the normal trading strategy. Whenever Dominari opens too many trades, the strategy then switches into reversal mode and trend trades with a simple trailing stop.

Switching direction should mostly reverse the negative trade skewness back in the positive direction. Almost all of the offending trades open at exactly the same time.

If the biggest losing trades opened at different times, there would be the risk of being too late to the party. All blowout trades opening at the same time means that the strategy can realistically reverse 100% of would-be losses into profits.

Sitting at the top of the docket are changes to Pilum. You can expect to hear about those soon so that I can incorporate Pilum into the Dominari signals. Once that and 2 other internal projects are finished, I’ll be able to dedicate the time required to fully implement the Dominari Reversal System.

Equity stop loss

Dominari uses emergency stop losses on all tickets. That is appropriate 99% of the time for individual trades. Those emergency losses reset once per hour in line with the concept of the TODS.

A little of the problem was bad luck. My stops came within a handful of pips of being triggered. Then they reset even further away, which made a bad problem worse.

When all trades move at the same time, then clearly the strategy could suffer extreme losses.

The first attempted solution after the Fed announcement was to add a portfolio level stop loss. The way that I wrote it also updated once per hour. When a second negative movement came in January, I stopped trying to be clever. It’s a flat, 単純です, stupid stop loss. If I lose more than 4% on all open trades, the entire Dominari portfolio goes flat.

I’m still trading Dominari

I still have my money trading the Dominari system; my confidence in the long term performance hasn’t changed, but it obviously requires safeguards. The max number of trades and the portfolio level stop loss will go a long way to limiting the impact of big moves in the future. AND, I should get the counter-strategy developed relatively soon to turn potential frowns upside down.

最後に, many of you questioned why I’ve been so quiet. The honest answer is that I needed some time to process what happened. It’s easy to feel overwhelmed and discouraged when you get knocked down. I needed some time to process what happened.

I also needed time to double check the changes that I made to the portfolio were actually beneficial. It’s very easy to appease traders when they’re upset by rushing out features before they’re thoughtfully considered.

My money is on the line (私は失いました 2,000 euros between the two moves). What hurt my subscribers hurt me, あまりにも.

取引プラットフォームの制限

10 月 18, 2015 によってショーンオバートン 6 コメント

この記事は、ベン Fulloon によって執筆されました, 尊敬されているトレーダーと OneStepRemoved にサブスクライバー.

ドローダウン比の素晴らしい戦略を開発しました。 13.67. 素晴らしいサウンド, 右? それは残念ですね 私の取引プラットフォームは二重以上に結果を誇張します!

それはあなたのブローカーとプラットフォームの制限の両方を学ぶことが重要です. 時には、これらの複雑さは、唯一の時間と経験を通じて明らかになります. あなたの取引プラットフォームは、機能やレポートの結果しない場合に予想されるように、それはとてもイライラ.

この記事では、NinjaTraderの2つの制約を指摘します 7, 実際に特定の状況ではトレーダーのための驚くほどより良いを回すことができるの悪い制限と1. しかし, これは私が使用しているブローカーではなく、プラットフォーム自体で行うことが多いです.

NinjaTraderは間違いなく限界を持っている唯一のプラットフォームではありません: メタトレーダー, 売買, X-トレーダー, Matlabの, など. すべての量的金融の制限があります.

私はかなり短く、読みやすいそれを維持するために、この記事でNinjaTraderについて書くことになります. 私もどちらか悪いのプラットフォームであるとしてNinjaTraderを作ることを意図するものではないのです. しかし, 定量的な開発するトレーダーやトレード戦略のためのそれは非常に簡単に、より便利にするために作ることができるいくつかの改善は間違いなくあります.

最初の癖は、私が使用しているブローカーに関連します. 具体的には, それは私が気にデイトレード・マージンです. この日の取引マージン終了 15 セッションの終了前に分. 例えば、ES (エミニS&P500) の日計り取引マージンが $500, これで終了 4:00その後の完全な取引マージンに戻り午後のCT $5060 セッションが終了する前に 4:15午後CT. (述べタイムズは記事の執筆時点で正確です, ESは、今で閉じ 4:00午後CTやデイトレードマージンがで終了 3:45午後CT)

私はあなたに私が開発した日の取引戦略の結果のスクリーンショットを紹介. この戦略は、ESの取引します, NQ (エミニナスダック 100) そして、YM (エミニダウ) すべて同時に. NinjaTraderと緊密に終了する最も簡単な方法は、どのセッションのクローズ時に終了し、次に意志をtrueに "閉じるに終了」を設定しています.

結果によれば、戦略は、の合計を作ります $332,771.60 最大ドローダウンと $25,912.27 以来、 2008 今. これは、のドローダウン比であります 12.84. それはoustandingです!

問題は...あなたは問題があると知っていました… 戦略はで終了するということです 4:15午後CT. 日の取引マージンがで終了 4:00午後CT. 戦略は、小さなアカウントサイズのマージンコールを取得することが可能性が高いです.

それは、一日の取引マージンを最大限に活用するための戦略を微調整することに意味が. Ninjatraderは、カスタムセッションテンプレートを提供しています, これはこのケースでは、私はで終了しました 4:00午後CT. 次のようにカスタムセッションテンプレートの結果があります.

マージンコールの作りを回避するために、同じ機器に適用まったく同じ戦略 $335,819.30 最大ドローダウンと $24,560.51. これは、のドローダウン比であります 13.67.

私は、ドローダウン比と利益の向上を目的とした戦略を変更していません. でもねえ, 買います. プラットフォームに制限を見つけることが実際にいくつかの状況であなたに利益をもたらすことができます.

この戦略は、取引に基づいています 3 異なる機器. ES, NQとYM. 問題は、私はバックテストそれがNinjaTraderに機器リストを使用していることです. これが意味することは、彼らはすべて個別にテストしているあります. NinjaTraderはその後、上記のスクリーンショットの結果などの総合結果としてあなたのためのテスト結果を組み合わせました.

ここでは、楽器のリストとしてそれらをテストするとき、それは次のようになります。. これは、個々の楽器の異なる利益とドローダウンを示しています.

今一見それはトレーダーが作ったであろうことを読み取り、 $335,819.30 最大ドローダウンと $24,560.51 彼らは一緒にすべての3つの機器を交換した場合. そう思いませんか?

問題は、これが間違っているということです. あなたが思うだろうようNinjaTraderは、実際に結果を結合しません. トレーダーは、まだおよそそのお金を作ったであろう. しかし, すべての統計情報は非常に正しくありません.

それは、ESを交換しますが、これを示すために、私は正確に同じ戦略を再作成しました, NQとYM同時にすべての代わりに、それはデフォルトではありませんように、それらを別々に取引. あなたは、マルチインストゥルメント戦略にそれをプログラムするときに、これらは結果であり、

それは作ります $335,915.30 これはほぼ同じ量であり、, それは、最大ドローダウンを持っています $59,937.60 代わりに $24,560.51 それは次のようになりますようにそれは、もともと見えました. これはそれのドローダウン比になります 5.60, オリジナルよりも多くの悪化しています 13.67.

トレーダーは、最大ドローダウンに基づいて取引をすることを決定した場合 $24,560.51, ドローダウンは、彼らが期待していたの倍悪いことが判明したとき、彼らは厄介なショックを受けること.

そのような重要なメトリックの誤った計算は、アカウントを危険にさらす可能性が. あなたが実際に戦略を取引するために必要なの株式の半分を離れて得ることができることを前提としていかもしれません. おっとっと?!?

NinjaTraderで誤解を招くような統計は、この戦略は本当に素晴らしい見えるのです. しかし、ドローダウンは、それが最初にあったであろうと思われた何倍以上である場合, あなたは厄介なショックを受けるかもしれません.

それは可能な限り早期にあなたのプラットフォームとブローカーの制限の両方を学ぶことが重要です理由です. あなたはこれらの制限に、ハードな方法を学習する必要はありません.

数週間の時間、, 私は、より正確な測定基準を示し、マルチインストゥルメント戦略を作成するための簡単な方法を明らかにします. シリーズの次回の記事をお楽しみに.

4/27

4 月 28, 2015 によってショーンオバートン Leave a Comment

現在の市場状況で起こって何かが明確に. Literally every currency pair is blowing out into a single direction. The addition of the recent pairs hasn’t done anything to stop it.

Rather than trying to fight this and hope and hope, it’s best to take a time out and see where the chips fall. I did spot a few issues with my long term trend direction that would have made this pain less bad – there is at least a small improvement that will come out of this.

ということで, the mantra for now is live to fight another day. It’s clear that the market conditions are beating the snot out QB Pro. I’m going to make the hard decision, which is to go flat and do nothing. This is exactly the reason that I don’t charge management fees – I’d feel awful trying to charge people for this recent performance.

I’ll update everyone in a week or two when we’re ready to evaluate whether or not to flip the switch back on.

どうなっているのですか?

4 月 22, 2015 によってショーンオバートン 9 コメント

それはどんな定義によるでこぼこ月をされています。. 先月の連銀発表の余波でお金のトンを作りました。, only to give it all back the next week. QB Pro recovered most of the earlier gains, then last week’s drawdown took it all back again. It’s been painful.

The good news is that the new changes to QB Pro are rolled out. Several of you sent in emails asking about new currencies like GBPNZD and AUDCAD appearing in your account. Kudos to you for paying close attention to the trading.

The total currencies traded in the basket is up to 16 ペア. While the max leverage is unchanged at 36:1 (still very, very high), the leverage per pair is only 2.25:1. Future losses like the one from last week will still occur.

The difference is that the size of the positions is reduced by over 2/3. The impact of getting caught in losing trades that are all reflective of USD weakness decreases significantly. We’re now trading a mix of AUD, CAD, スイスフラン, ユーロ (EUR), 英国ポンド, 円, NZD, USD and XAG. No one currency should dominate the performance.

The system also does extremely well on emerging market currencies. I’m holding off on adding RUB, MXN and others until I determine the impact of the spreads on overall profitability. They’d do amazing if we could trade for free!

Short term performance expectations for QB Pro

We’re coming into the summer, which is when the forex market traditionally falls into the doldrums. That’s generally a good thing for QB Pro. The markets whipsaw up and down without really going anywhere.

The alternative is that the Fed hikes rates in June and sends the market into a USD buying frenzy. That’s also good news. Most of the money that QB Pro made over the past 8 months was driven by USD strength. A rate hike would unleash chaos in emerging markets and equities. That’s the kind of condition to push volatility into our new crosses, creating opportunities for us to trade.

QB プロ 2.0 isn’t happening

I’m extremely disappointed. After several thousand dollars in programming expenses, and not to mention the 100+ hours that I spent coding myself, the QB Pro 2.0 change is a wash.

I had a trusted developer audit my code to make sure I wasn’t doing something stupid like trading on future prices or anything. Neither him nor myself caught anything from December until March.

Towards the end of last month, a single line of code ruined it all. One of my key features was deciding when to bail on trades and go the opposite direction. まあ, it turned out that I accidentally introduced data snooping into the backtesting platform. I pre-calculated when losing trades occurred to calculate probabilities.

In plain English, my goal was to calculate “If today was a big loser, then do the opposite tomorrow.”

What I accidentally coded was “If tomorrow is a big loser, then do the opposite.” If only that were possible!

I don’t want to muddle up the explanation with code examples. Suffice it to say that the idea didn’t work out when I took away the ability to look into the future.

There are some features of the 2.0 system that I wish to analyze in the coming months, but for now it’s going to have to take a back seat.

What’s next?

My plan is to sit tight for a few weeks to ensure that the new pairs are working as intended. Whenever I am personally satisfied with the system behavior, I intend to increase the amount of capital in my account.

Don’t hold my feet to the fire. This part is a subjective process, so I can’t put a precise time frame on it. If and when I am satisfied – and it’s going very well the first few days – then I will make a decision about increasing my capital at risk.

If and when I choose to increase my capital in the account, I will then re-open QB Pro to new traders.

PS: I hope that the drawdowns encourage some of you to withdraw profits the next time the opportunity presents itself. You don’t want to lose more than you are comfortable risking.

最大レバレッジを使用して利益を成長し、リスクを軽減

1 月 12, 2015 によってエディ・花 9 コメント

The gains can accumulate quickly when a prop trader is using a strategy based on maximum leverage with limited account size. In order to preserve and build those gains, it’s important to remove them from the trading account according to a good plan.

As described in previous articles in this series, the high-leverage, low-balance strategies used by leading prop traders can be applied to multiple trading accounts using different systems, with each account capitalized by not more than a couple thousand dollars.

The amount in the account typically ranges between $1,000 to several thousand dollars. その方法, there’s no psychological obstacle to using the max leverage on each trade.

Reduce the risks from drawdowns

When you have a winning system, profits pile up. It’s tempting to “let it ride” by using the same system to trade ever-bigger position sizes in the growing account.

しかし, when the entire capital is available in the trading account, it means that the capital is exposed to the inevitable system “blow up,” which typically causes a steep drawdown. Even if the trader escapes financial catastrophe, he or she may become so risk-averse afterward as to become indecisive and ineffective.

Pull money out each month

The smart way to avoid excessive drawdowns due to trading system “blow ups” is to pull money out of the account at the end of each successful month. その方法, when a major drawdown occurs, it won’t take all your money, just the couple thousand dollars that you can afford to lose.

Successful prop traders like Shaun sweep the profits out of each winning trading account monthly and move them into a non-trading account, where they remain safe. だから, each month the trading accounts open with their individual capitalization set at a given amount.

Pull out at least enough to cover one “blow up”

Once you’ve launched your forex system, you’ll want to think about earmarking enough money to cover at least one trading system failure. After you’ve secured that amount to be used for a recapitalization of your trading account, every subsequent gain is “free money,” at least in a psychological sense.

The first milestone is to pull enough money out of the trading account to cover at least one catastrophe. If you’ve been enjoying mostly winning months, next you should allocate 50% of your profits for high-risk systems.

You can’t lose what’s not at risk

覚えておいてください: When a prop trader is using maximum leverage, the only money that’s safe is the money already pulled out of the trading account. Profits should be pulled from each winning trading account, each month.

When a prop trader wins consistently using high leverage with a limited-size account, the gains from relatively small individual trades may compound quickly. Profits gathered from the overflowing small trading accounts can compound into large sums, and it’s important to manage those profits effectively.

If you’d like to learn more about using maximum leverage to pull profits each month, just contact Shaun.

What Quantitative Value Do Stops Actually Have?

12 月 20, 2013 によってアンドリュー・セルビー Leave a Comment

One of the questions that every quantitative trader must address is whether adding a stop-loss component to their system will help or hinder its performance. I have written a good deal lately about the pros and cons of different types of stops, but haven’t had much actual backtesting data to work with.

When I wrote a post about about Cesar Alvarez’s S&P Rotational Strategy a few weeks ago, I suggested that adding a stop-loss might lower the maximum drawdowns. This would give the strategy a way to exit losing positions during the month, rather than waiting for the monthly redistribution. Theoretically, this would have reduced some of the big losses that the strategy suffered in 2008.

We assume that adding a stop loss component has the quantitative value of a safety net, but that isn’t always the case.

In addition to writing about that idea here, I also commented on Alvarez’s post. In response to that, he has written a follow-up post addressing my suggestion to implement stops:

Continuing from the post, we are adding a maximum stop loss. The stop is evaluated at the close each day with the exit happening at the close. The tested stops are 5%, 10% と 15%.

興味深いことに, Alvarez finds that adding stops can be helpful in some situations and terrible for performance in other situations. While adding stops may always seem like a logical idea in theory, Alvarez shows that actual performance can prove otherwise.

Best Performing Stocks

The version of the best performing stocks strategy that we looked at in the previous post utilized a market timing indicator and a six month look-back period. That strategy produced a CAR of 10.48% 最大ドローダウンと 42.22%. Here are the numbers when 5, 10, と 15 percent stops were added:

5% 停止: 10.51% CAR, 26.30% MDD
10% 停止: 10.85% CAR, 38.05% MDD
15% 停止: 10.84% CAR, 39.48% MDD

あなたが見ることができます。, adding the stop loss doesn’t do much for the CAR, but it does a great job of reducing the maximum drawdown. When the stops were applied to the version of the strategy with a 12 month look-back period, the impact on maximum drawdown was similar, but the CAR saw a bit more of an increase. When the stops were applied to each of the two versions without the market timing indicator, we saw a slightly less impact on drawdown and a much greater impact on CAR.

Alvarez also commented that in almost all cases, 、 5% stop was the best performer, which he thought was unusual:

Normally close stops tend to be the worst but the 5% stop tends to be the best.

Worst Performing Stocks

The worst performing stocks version of the strategy that we looked at used the market timing indicator and a six month look-back period. The strategy without stops had a CAR of 13.05% 最大ドローダウン 27.88%. Here are what the numbers look like when the different levels of stops were applied:

5% 停止: 5.11% CAR, 28.26% MDD
10% 停止: 8.36 CAR, 30.90% MDD
15% 停止: 10.47% CAR, 30.87% MDD

この場合, adding the stops has really hurt the strategy. While there was some improvement in the maximum drawdowns of some of the versions, adding stops basically crippled the CAR of all of the worst performing stocks strategies.

Alvarez notes that this is the result he expected:

For the worst N-month ranking, stops appear to hurt the all results. These results support previous research that stops on short-term mean reversion hurt results.

Keeping up with the humans

10 月 10, 2013 によってショーンオバートン 2 コメント

Daniel Fernandez posts a nice summary of some of the problems algorithmic traders have experienced over the past few years. If you’ve been wondering why your expert advisor isn’t making money, まあ, you’re not alone.

Daniel points out the terrible performance of the Barclays systematic trading index and its nearly three years of continuous losses. Even the pros are losing money consistently.

Tough Times with Algorithmic Trading

The performance of professional systems traders has fallen over the past two years

Key sections:

It is no secret that algorithmic trading had some “golden years” between 2008-2011. Through this period – most notably due to the high directional volatility of the financial crisis – systems based on a wide variety of market characteristics were able to obtain high amounts of profit, with an almost completely negative correlation with equity markets. Among the high-performers found during this period, trend followers were perhaps the most impressive, with some systems achieving returns of more than 100% of capital within this period, with little drawdown whatsoever. During these years everyone trading algorithms was making a killing. その後、, change happened.

The answer seems to be simple and at the same time incredibly complex: fundamental influence and uncertainty. Algorithmic trading systems are all designed with the idea that some historical assumption will continue to be true in the future. This assumption can be that price tends to break at a certain hour, that momentum created in one direction leads to continuations, that two instruments are co-integrated, など. When these assumptions break, the algorithms fail because they have no way to know that under current market conditions their assumptions are no longer valid. This “breaking up” of algorithms means that we usually need to take loses to realize that something has changed – to remove or modify our strategy – and this makes us invariably less reactive than human traders. The strength of algorithmic trading, it’s high capacity to exploit structural characteristics, becomes its weakness when the underlying structure changes.

Backtesting Biases and Variations

10 月 3, 2013 によってアンドリュー・セルビー 4 コメント

先週です, I wrote a post discussing how altering the timeframe of a system can change its results. That got me thinking about other ways that backtesting results could be skewed in one way or another based on user defined data such as the date range and market used. These simple differences can have a tremendous influence on the overall returns of any system, so it is important to pay them their proper respect.

When running backtests, it can be very easy to gloss over the down periods and cherry-pick the big return years. The problem is that you won’t have that opportunity when actually trading a system live. You will need to prepare yourself for the possibility that you select the wrong time or the wrong market to trade a given system. それ以外の場合, you run the risk of letting these backtesting biases adjust your expected return to values the system cannot possibly deliver.

Adjusting the Date Range

Let’s use our 10/100 Moving Average Crossover System from last week as a base. We tested it from January 1, 2001 12 月まで 31, 2010 on the Vanguard Total Stock Market ETF (VTI). All of our tests last week used a starting portfolio value of $10,000, 、 10% トレーリングストップ, と、 $7 委員会.

MA crosses on VTI returned almost 90% over the last decade.

Based on those settings, our 10/100 MA Crossover System returned 89.8% over ten years. This works out to be an annualized return of 12% 最大ドローダウンと 16.2%.

If we would have started trading this system on January 1, 2003, we would have registered a total return of 39% in the three years of trading until the end of 2005. This would have been good for a 16.4% annualized return with a maximum drawdown of only 6%. あなたが見ることができます。, if we based our strategy on these results, we would be expecting the system to continue to produce these extremely high returns.

反対に, if we would have started trading this system on January 1, 2006, we would have seen a total return of only 2.5% in the first three years. We also would have had to sit through a 14.2% ドローダウン.

It is also worth noting that while the ten year track record of this system from 2001 を通じて 2010 is very respectable, we wouldn’t have known that when we started in 2001. If we actually started trading this system in 2001, we would show a total return of -6.2% at the end of 2002. After two full years trading this system, we would not have had a single thing to show for it. The system didn’t find its first big winner until April 15, 2003.

あなたが見ることができます。, the time you chose to begin trading the 10/100 Moving Average Crossover System could have made all the difference over the course of what was a net-profitable decade. It is very important to keep this in mind when you are struggling through drawdowns.

Adjusting the Markets Traded

The market you choose to trade can have the same affect on your trading as the date you start trading. Let’s look at how the exact same system would have performed over the exact same decade if we chose to trade it on different ETFs.

Trading the 10/100 Moving Average Crossover System on the XLF, which represents financials, would have provided a total return of -9.4% for the decade with a maximum drawdown of 30%. It is obvious to us at this point that financials had a rough time during this period, but we would have had no clue about that when we started in 2001.

The XLY, which represents consumer discretionary stocks, also would have underperformed the VTI. Trading the system on the XLY would have returned a total of 39.4, または 6.4% 毎年, 最大ドローダウンと 21.3%.

XLY shows a 39.4% return over the same decade

If we would have been fortunate enough to trade the XLK, which represents the technology sector, we would have seen a tremendous total return of 95.7%. This works out to be an annual return of 14.2% 最大ドローダウンと 22.3%.

もう 1 回お願いします, we see that decisions like what markets to trade and when to start can have a tremendous influence on our results. This is why it is so important to thoroughly backtest any strategy across many different combinations of date ranges and markets.

新しい年次高システム

9 月 25, 2013 によってアンドリュー・セルビー 2 コメント

ほとんどの傾向のルートに下記のシステムが考え動いている長い市場を高くするにはまたは短い市場が下に移動. アップトレンド、ダウンは新しい 52 週高値または安値を作っているものを探す、市場を特定する最も簡単なと最も人気のある方法の一つ. この単純な信号は、システムを次の傾向に従う非常に単純なのと簡単なルートとして使用できます。.

システムについて

彼の本, 邪悪な杯, 著者とニック Radge のトレーダーについて説明してルールを設定し、バックテストの結果を提供するさまざまなシステムの数. 彼について説明します、最初のシステムの 1 つは新しい年間高値を作って株が高い続ける可能性が高い概念に基づいて, そして、毎年安値を作る株が低い傾向を続ける可能性が高い. これは戦略を次の最も体系的な傾向の背後にある主要な概念の 1 つ.

Radge は指摘する新しい年間の最高値に基づいているシステムとほとんど財源株式新しい 52 週高値と安値のデータを公開するため、安値は非常にユーザーフレンドリー. つまり、任意の取引のソフトウェアを使用せず手動でこのシステムを文字通り取引ができました.

Radge は、システムを簡素化する 1 つの方法があると仮定すると、します。 250 取引年日. したがって, 新しい年次高または低ハイまたはローに新しい 250 日を作る任意の在庫は作っても. つまり、我々は非常に単純な 250 日ブレイクアウトシステムとしてこのシステムを扱うことができます。.

このシステムを確立 [開くロングポジション在庫 250 日高値になり、株価は 52 週安値翌日のオープンにその位置を終了した翌日. これらの 250 日間の高値と安値の価格チャンネルオーバーレイを使用して監視するな, ほとんどのグラフ作成パッケージで利用可能であります。. 表す下のグラフ上の黄色実線 250 日高低価格します。.

BND を新しい形成します。 250 1 日少ない

取引のルール

長いときを入力します。:

価格は、新しい 250 日高で終了します。

長いときを終了します。:

新しい 250 日低価格終了

バックテストデータ

このシステムのバックテストに順序で, Radge すべての株価指数の銘柄を使用, オーストラリアで最も人気のある株式市場インデックスです。. 彼はすべての株価蓄積指数に彼の結果を比較します。, 購入を表し、オーストラリア株式市場にアプローチを保持するベンチマークであります。.

彼の試験期間を 1 月から走った 1, 1997 6 月から 30, 2011. アカウントが始まる $100,000 位置のサイズ 5% アカウントの. 手数料や配当金は考慮したも.

これらの新しい年次高いシステムを取引 14 年との合計を作り出した 170 取引. 年複利成長率 (CAGR) 印象的なされているだろう 18.21%, 2 倍以上、 8.78% ベンチマークの CAGR を購入し、アカウントを保持. システムに収益性の高い 53.3% その取引のシャープレシオを掲載 0.395.

バックテスト結果の明白な負の最大ドローダウンを新しい年次高システムに掲載になる上 50%.

システム解析

システム開発について聞く、最も一般的なものの 1 つは、トレーダーがエントリにあまり重点を置くし、出口の信号. 逆に, リスクマネジメントとポジションサイジングを十分重視は一般にありません。. このシステムは、その入口と出口の信号が正常に動作する表示されるのでこれらの哲学の良い例, しかし、その 50% ドローダウンは、その利益を保護すること十分な仕事をしないことを示します.

新しい年間高値システムのかなりの強さそれが動作. そのリターン実際にビートを購入して 2 倍以上によるアプローチを保持. しかし, その弱点に対処する必要があります。, それを介して座っている間システムに固執しようとする絶対に悪夢になるので、 50% ドローダウン.

システムの改善

トレンドフィルター

最初にこのシステムを改善するために行うだろうことはトレンドフィルターを導入します。使用して、 200 日単純移動平均 (SMA). このフィルターは単に一般的な市場が、任意の 1 つの株式のロングポジションを入力するシステムのために上昇傾向にあることを要求します。. これにより、システムは downtrending の一般的な市場に対してより高い傾向にしようとしていた株式のロングポジションを取っていません。. 我々は一貫して貿易の全体的な市場の傾向の場合、私たちはより多くの全体的な成功を持っているべき.

トレーリングストップ

システムに追加するだろう別の条件になる、トレーリングストップの ATR ベース我々はすべての収益性の高い取引からの利益でロックされていることを保証すること. それはまた、負けの損失を最小限に抑える. 最終的になっている有益な短い貿易を切断することによってビットを返しますこの停止が全体を減らすかもしれないを追加, 欠点の保護はその犠牲の価値がある可能性が高いだろうが、.

宇宙の変更

Radge を示唆しているものの 1 つは個々の株式ではなく市場インデックスに専用システムを取引します。. 彼は提案するこれがボラティリティを削減するだろうし、バックテスト結果とその概念を証明します。. このアイデアに CAGR を下げた 13.92%, しかし、また下に最大ドローダウンをカット 36.03%.

インデックスのシステムを取引のボラティリティを削減する思考のラインを維持します。, 私は複数のインデックスまたは Etf 取引システムをテストするのにはちょっと気になります。. Etf の取引のシステムが実行した方法を見るは興味深いだろうアイビー 10 システムまたは、 Andreas Clenow おすすめ市場で次のトレンド.

Why You Must Design Your Own Trading System

5 月 20, 2013 によってアンドリュー・セルビー Leave a Comment

After deciding to explore system trading, many traders are tempted to expedite the process by purchasing someone else’s system. These traders are often met with disastrous results.

In order to successfully trade a system, you must have unshakable confidence in that system and the system must fit your personality. The only way to achieve this is to build your own system.

Confidence In Your System

No trading system is perfect. All trading systems experience drawdowns. The difficult part comes when you try to determine if a drawdown is normal or if markets have fundamentally changed in a way that erases your system’s edge. The only way that you will be able to tell the difference is if you know your system inside and out. That only happens when you build it yourself.

There are many trading systems that you can purchase for a wide range of prices. Some are based on solid strategies. Some are over fitted to a specific style of market. The problem is that if you jump into using someone else’s system, you won’t know how it was designed to handle different markets. When the system experiences a drawdown – it’s going to happen – you won’t know whether or not that drawdown is normal.

Shaun wrote a post earlier this year discussing drawdowns. He referenced how Dustin Pedroia struggled when the Boston Red Sox first called him up to the big leagues. The Red Sox stuck with their young second baseman because they were able to identify that his low average was not sustainable because of his high contact rate.

Shaun compared the Red Sox sticking with Pedroia to a trader sticking with his system during a drawdown. The Red Sox were able to understand Pedroia’s slump because they were able to look deeper into his performance statistics. A system trader can do the same thing if he knows his system well enough to look deeper into its performance statistics.

Pedroia stays in the lineup during a drawdown

The Red Sox stuck with Pedroia during a slump because they knew how good he was

By taking the time to backtest a trading system through different market periods, you will gain an understanding of how the system reacts to different types of markets. たとえば, if your system creates most of its profits during a trending market, then you won’t have any reason to panic if it experiences a drawdown during a non-trending period. That would be expected. However if the same system was struggling to produce in a trending market, you would be more concerned.

Building A System The Fits Your Personality

Another reason that you must construct your own trading system is that the system needs to fit your personality. The system used by The Turtles is probably the most famous system of all time, and you can purchase software that trades that exact system for less than $1,000. The problem with that approach is that you might not be a good fit to trade the way the Turtles traded.

The amount of trades a system makes, the time frame that those trades are held, and amount of capital risked on each trade are all factors that affect how a system suits your personality. While the Turtle’s system worked for some traders, if that system exposes your capital to more risk than you are comfortable with, then you won’t be able to sleep at night. While each of these factors can be adjusted during the process of building a system, many black box systems do not allow for adjustments. また, without backtesting data, you won’t be able to determine how these adjustments will affect the system’s performance.

Going back to Shaun’s Dustin Pedroia reference, the Boston Red Sox were able to have confidence in the numbers of their short, odd-looking prospect because he fit their personality. They also had great success when they acquired corner infielder Kevin Youkilis, who was not an outstanding hitter, but had an exceptional ability to draw walks. If either of these players made the front office feel uncomfortable, they never would have been successful.

Every element of a trading system must reflect your personality

Youkilis is a player that makes the Red Sox comforable. Are you comfortable with each element of your trading system?

Acquiring players like Pedroia and Youkilis fit the personality of the Boston Red Sox, who were obsessed with crunching numbers and didn’t mind looking foolish if they were wrong. I would compare this to trading a system that focused on absolute return that also expects steep drawdowns.

On the opposite end of the spectrum, the New York Yankees are known for acquiring players that are already proven commodities later in their careers for much higher salaries. This approach is more in line with a trading system that trades for smaller profits with much less risk.

OneStepRemoved.com is dedicated to helping traders find a system appropriate for their personalities. Email info@onestepremoved.com and let us know how we can help with your trading.