アルゴリズムと機械の外国為替戦略 | OneStepRemoved

勝利の取引システムを作成するには、方法

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脂肪質の利益のための機会のため、多くのトレーダー、外国為替に魅了されて, 株と比較すると特に. まだ, when trading forex the inherent leverage can affect traders’ emotions, leading to over-trading, loss-chasing and second-guessing. A mechanical trading system can provide the winning solution.

Why build a trading system?

Manual trading works well for many stock traders, especially those using buy-and-hold strategies for a limited number of favorite picks, yet forex traders need better tools and stronger discipline in order to be profitable.

In any industry, a well-built machine is more efficient than any human

A well-built mechanical trading system offers a trader the best of both worlds: technology and math give the trader the ability to spot and take advantage of market inefficiencies and harvest gains in a busy, cluttered environment, while freeing him or her from the emotional roller-coaster ride of trading.

Find your own niche

There are plenty of trading systems available nowadays; the key to forex trading success lies in finding or adapting the “right” system for your own needs and style. Once you’ve decided the parameters for success, including your overall goals and objectives for trading, personal tolerance for risk, and the amount of capital to be devoted to trading, a system can be built to fit you like a glove.

When building a system, there’s plenty of room for specialization and individualization – If everyone were trading the same way, spreads would soon disappear. Like fast-moving mosquitoes buzzing around a lumbering elephant, many traders earn an excellent living by capitalizing on opportunities inevitably created by the movements of much-larger players in the marketplace; the key is to gather an actionable set of patterns and indicators that fits your personal style.

If a pattern is noticeable, then it’s probably actionable

The first step is to search through past trading data in order to identify patterns and conditions which appear to consistently offer profitable trading opportunities. Historical price and volume charts often show patterns which appear to signal upcoming price moves, and technical indicators will help clarify an otherwise-fuzzy picture.

Try looking at different combinations of indicators over different historical time periods to see if they may give predictive power in spotting market turns or changes in trend. A “caveman-style” approach to quickly testing your hunches can be as simple as finding a noticeable pattern on a printed chart, then holding a sheet of paper over the upcoming section and “guessing” what will happen next; when you’re right, you may have found a winning pattern.

テスト & 最適化

Once you’ve identified a fairly-predictable pattern by looking at charts, it’s time to think about how to trade it profitably. You should consider how it fits with your personal trading style, including risk management. The patterns and indicators upon which your system is based can be simple or complex, as long as they work in the marketplace and fit your circumstances.

How to create a winning trading system

The next step is to translate these patterns and scenarios into mathematical coding, to form a set of trading rules which can be fully tested. You can do this yourself, or you can rely on the services of a coding expert to help accomplish this. After you’ve created the foundation for a system, it can be tested objectively by changing the inputs to find the optimal conditions for trading, such as the best combinations of currency pairs, 停止, and other variables.

You can use software to quickly test multiple combinations of indicators. The key is to identify predictable patterns which will give you the confidence to trade when you see them appear, 長いか短いかどうか, then fine-tune them to maximize your gains. It’s important to realize that more complexity isn’t necessarily better – A super-complex system probably won’t fatten your wallet if it only signals a trade once every ten years and your computer happens to be offline when that finally occurs.

Don’t become married to your system

最も重要なこと, if your indicators aren’t working out during testing as you had hoped, don’t become emotionally invested in “proving” that they work. 代わりに, step back and take a broader look – Perhaps it’s time to use a different combination of indicators, or change your approach altogether.

During testing and optimization, it’s important to leave untouched some of your historical market data as untested “out-of-sample” data while you work through testing your system using in-sample data. For statistical purposes during testing, you can only use data once before modifying your system; then of course it becomes part of your in-sample data. If you contaminate your test data, それです, if you rely on a certain date range of data to first develop and test your system, then later re-test your modified system with the same data, the results may be skewed. だから, use your out-of-sample data only for final testing and tweaking 後 you’ve built your system, so you can be sure that such data is “pure” and not already accounted for in the system.

Be sure to back-test any prospective new system over reasonably long periods, so you’ll have an idea how it performs long-term. と, check the results when using different lengths for your moving averages. また, it’s worthwhile to test your system widely across different forex pairs, even those you don’t typically trade – You may be surprised to find that your system does especially well in a market that you haven’t tried before.

Implementation

Even though testing and minor tweaking should be thought of as an evolutionary process that continues during the life of your trading, at this point you’re ready to implement your system by using it to trade with real money. If you’ve done your homework well, and you stick to the rules that your testing has proved will work under specific conditions, then you’ll be confident in proceeding forward.

Stick to the proven rules and you’ll be successful

Societies rely on laws to govern the behavior of their citizens because they’ve learned over time (tested and optimized) what works. 同様に, in order to be successful with forex you should adhere to the consistent trading rules that you’ve established in a scientific manner. If you stick to the rules, your mechanical trading system can help you win the forex game.

サンプルサイズの懸念を説明するためにNFLの使い方

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私の自由な時間に, 私は、メジャーリーグの試合に賭けるための定量的な戦略の開発に取り組んできました. これは非常に興味深いサイドプロジェクトとなっています, 現時点では、それは非常に成功していません.

私が代わりにサッカーやバスケットボールのゲームの野球の試合に集中することを選んだ主な理由は、野球チームは、他のスポーツよりも多くのゲームをプレイすることです. 時間をかけて, これは、より大きなサンプルサイズを提供するべきです, 私はより多くの重要な結果を与え、小さなサンプルサイズに起因する差異をなくします.

サンプル サイズ

NFLは、小さなサンプルサイズの統計的有意性の観点から避けるために、何の優れた例を提供してくれます.

同様にサンプルサイズのトピックを見て月曜日にGestaltuの投稿がありました. 著者は、自然を使用しました NFLで発生復帰を意味します 例として、すべてのシーズンが影響を説明すること サンプルサイズが小さいです パフォーマンスに持つことができます. 記事はまた、小さなサンプルサイズは、当社のファンドマネジャーの評価やバックテスト結果に導入することができる危険性に対処.

ゲームの数は、事項を再生しました

NFLでレギュラーシーズンの構成は 16 ゲームで再生 17 週間. 比較して, NBAやNHLの各プレイ 82 シーズンゲーム, そして、MLBチームは遊びます 162 ゲーム. 各リーグのチーム数により、これらの合計を掛けて、あなたは他のどのスポーツよりも大幅に少ないNFLゲームが毎年あることがわかります.

ここでは、取引の比較は明らかです. 私たちは真剣に基づいた戦略を検討することは困難であろう 16 取引. 作る戦略 162 取引毎年不運な取引を回避するためのより良いチャンスがあります, あるいは、少なくともそれらからの回復.

平均チームはプレーオフを作ります

記事では、毎年NFLでプレーオフに潜入平均才能レベルのチームがあることのポイントを強調. そのため、小さなサンプルサイズの, これらの平均のチームは、シーズン中にいくつかの幸運休憩から莫大な報酬を得ることができます.

同じやり方で, 彼らのパフォーマンスの歴史の中で同様のラッキーブレイクからの多くのファンドマネジャーの利益. 記事はさらにさかのぼるレコードを追跡することを示しています 10 統計的に有意であるのに十分に大きいサイズのサンプルを欠くことができる年.

平均サッカーチームがプレーオフに潜入することができ、同様に限界ファンドマネジャーは、印象的なリターンを投稿することができます, 彼らの理想的な環境でのバックテスト時の平均取引戦略は、優れたリターンを生成することができます.

むしろバックテスト結果のみに焦点を当てより, 我々はまた、戦略の基盤となるプロセスを見てする必要があります. 我々は、我々の戦略は制限したいです 入力パラメータの数 かつ徹底​​的な統計分析に耐えすることができます. あなたは、資本のすべてがに乗って巻き込まする必要はありません 2012 中にボルティモア・レイブンズ 2013 シーズン.

我々 はそれほど頻繁に取引することによってリターンを増やすことができます。?

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“それは、ほとんどすべての理論の最高の目標は、経験の単一のデータの適切な表現を放棄することなく、可能な限りシンプルで、わずか既約の基本的な要素を作ることであることを否定することはできません。” – アルバート・アインシュタイン

アインシュタインからの引用は見Gestaltuの最近のポストの後ろのインスピレーションでした さらに簡素化する方法 すでに簡単な回転戦略. 投稿はメバネフェーバーの非常に人気を取りました アイビーポートフォリオ そして、同様の戦略は、毎月のではなく、年ごとに一度だけ、ポートフォリオをリバランスすることによって同等の結果が得られたことができるかどうかを試験.

ivy portfolio

それだけで年に一回、それを取引することにより、毎月のリバランス戦略を向上させることが可能です?

それは私がフェーバーのアイビーポートフォリオの大ファンだということは秘密ではありません. 私はそれが取引にマルチ戦略的なアプローチを構築するための優れた基盤を表していると信じています. 結果は、そこに最高のすべての周りの長期戦略の一つであることを示しています, 同時に従うことが非常に簡単です.

この理論の試験のため, 著者は、取引アイビーポートフォリオのバージョンで開始 5 その10ヶ月移動平均に基づいて、資産クラス. ポートフォリオは、彼らがリバランス時に、その移動平均の上または下にあるかどうかに基づいて、資産クラスのそれぞれについて、長いまたは現金でのどちらかであります.

バックテスト結果

可能なリバランス日付の広い範囲に基づいて提供数の圧倒的な量がありました. 結局のところ, ほとんどすべての場合には, 毎月のリバランスは、毎年恒例のリバランスを上回りました.

著者らは、取引暦の各個々の日に毎年恒例のリバランスをテストする限り行ってきました. いくつかの日は、毎月リバランスをアウトパフォームすることができましたが, そのパフォーマンスがありました 運に起因します, むしろ、再現エッジより.

ポストはまた、10ヶ月移動平均の代わりに、12ヶ月移動平均を使用して実験を行いました. その場合, リターンはほぼ同一でした.

我々は、他の方向に行くことができます?

記事では、リバランスの頻度と取引コストとの関係に関する議論で締めくくり. 明らかに, 毎年リバランスの代わりに、毎月、トランザクションコストの多くを救います, パフォーマンスが価値ではありません. 反対に, リバランスの毎日は、戦略が、潜在的なエッジを失うので、多くの取引費用を負担.

記事が上に触れていなかった1つの領域は、毎週のリバランスでした. これは、毎月のリバランスよりも仕事と高い取引費用を表すことになります, それでも毎日リバランスよりもはるかに少ないの取引になります. 私は、毎週または隔週をリバランスすると、毎月のリバランスを超える改善であるかもしれないかどうかの好奇心.

3 つの異なる方法がサバイバーシップ ・ バイアスは、バックテストを傷つけることができます。

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ようだ学ぶ開発とテストに関して定量的な取引戦略, 多く我々 のバックテスト結果を台無しことができますどのように簡単に実現します。. 我々 は完全に無駄なテスト結果を生成できる最も簡単な方法の 1 つは適切に洗浄されていないデータを使用して、します。.

サバイバーシップ ・ バイアスが多くの微妙な方法で私たちのバックテストは、価格データにクリープすることができます。. として取引価格の違いによって、3 つの最も明白な方法の, 上場廃止株のインクルードに失敗, か、今日ではなく、歴史的なコンポーネントを使用して構成されているとインデックスをテスト.

サバイバーシップ ・ バイアス

サバイバーシップ ・ バイアスの多くの異なるバージョンまでのバックテスト結果を開くことができます生き残るために十分な強されている株式のみを含む.

これらのデータ セットの欠陥のそれぞれは、バックテスト結果を様々 な影響を持つことができます。, 我々 はテスト戦略と他の変数の数の種類に応じて. で、 最近のポスト, セザール アルバレス サバイバーシップ ・ バイアスの異なるバージョンに含まれるデータを使用して、いくつかの戦略をテストするのには時間がかかった. 彼の結果を示したときバイアスは、ごくわずかな影響を持つことができます時間があること, しかし、影響が重要な場合もあります。.

インデックス コンポーネント

年間を通じて異なる時期に, 各主要なインデックスは、インデックスを構成する株式を調整をしていく. これは特定の方法で一般的な市場を追跡するインデックスの機能を維持するために行われます.

バックテスト宇宙としてインデックスの今日のバージョンを構成する株式のみを使用して, 我々 はすべての私たちのバックテストの長さのインデックスから削除する株式不十分な十分な実行を排除する自動的に. これは我々 が実際にあろうその期間ライブ システムを取引した場合のバージョンよりもはるかに強い株式の宇宙と私たちを葉します。.

セザールの研究は私たちの毎年の収益を投稿することができた方法を示します 36.25% 最大ドローダウンと 24.54%. しかし, 正確な同じ戦略がインデックスの歴史的に正しいバージョンでテストされたとき, 年利に落ちた 14.07% 最大ドローダウンに上昇しました。 30.42%.

上場廃止銘柄

上場廃止株が最も一般的理解の状 サバイバーシップ ・ バイアス. これらは、彼らはもはや買収や倒産により取引されているので、バックテストを欠場する株式. 不要になったインデックスに記載されている株式と同様, 私たちに我々 はライブ取引中にあろうよりも株式の強力な宇宙を与えること我々 の宇宙で上場廃止株の失敗.

一貫性がないので、セザールをこの場合提供する証拠は面白い. テストするとき、 平均回帰戦略, 実際にパフォーマンスを向上させるが表示されます上場廃止株式を含む. しかし, テストするとき、 戦略を次の傾向, 年間収益率と最大ドローダウンにマイナスの影響を持っていた上場廃止株式を含む.

として取引価格

セザール試してみたかったもう一つのサバイバーシップ バイアス アイデアはバックテストのパフォーマンス分割調整価格の影響. 株式分割のため, 個別銘柄の歴史的な分割調整価格は当時実際にあったよりもはるかに低い多くのインスタンスがあります。.

セザールの理論はこれが持っていることバックテストに負の影響を検討すると株の最低価格を必要とする戦略. これは非常に合理的な音ながら, 結果表示の違いはほとんどの場合ほとんど無視できます。.

あなた自身の外国為替の戦略を作成するための青写真, Part 2

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Earlier this month, we looked at an article from Forex Crunch that covered the first three steps for building a new quantitative Forex strategy. Those first three steps covered brainstorming strategy ideas, defining the rules, and optimizing the parameters.

At that point we had a strategy that we had reason to believe would perform well in a trading situation. The next steps would involve properly testing our strategy in order to prove its value.

外国為替戦略

After brainstorming, defining rules, and optimizing a new Forex strategy, the next steps involve rigorous testing.

Forex Crunch has since published the second three steps for creating a robust Forex system. This post focuses on testing the system that was created with the first three steps. It suggests starting with in-sample testing, then moving to out-of-sample testing, and then suggests some even deeper methods of testing.

The Most Important Point Regarding Testing

While there is plenty of great information in the article about the different types of testing that should be performed on a new Forex strategy, the most significant point that the article makes is actually stated in the introduction:

Relying on the CAR (compound annual return) figure is not always a good idea because this metric does not take into account the risk that was involved in producing those gains.

This point is extremely basic, which makes it easy to overlook. While a strong compound annual return is the end goal of every trader, we all know that there are many ways to arrive at a strong compound annual return, and some of them aren’t worth the effort.

In addition to compound annual return, we also need to be concerned with how the strategy performs from a risk perspective. Looking at statistics like maximum drawdown, プロフィットファクター, Sharpe ratio, and winning percentage gives us a better idea of how the strategy arrives at its compound annual return.

This bigger picture view will give us a more qualified overview of what trading the strategy will feel like. We can use that to determine if the amount of risk the strategy exposes our capital to is in our tolerable range.

Testing Forex Strategies

Testing on in-sample data is where we can fine tune our strategies in order to get the return and risk statistics into the desired range. From there, we move to out-of-sample testing where we attempt to replicate those statistics on a fresh data set.

There are also testing methods like Walk-Forward Optimization and Monte Carlo Simulations that can shed even more light onto how our new system can be expected to perform in live trading. The important thing to watch for during this testing phase is consistency. The strategy should perform similarly across all of these different types of testing.

If the strategy produces solid returns through a wide range of testing, it can be expected to produce similar results in live trading.

Are We Wasting Our Time with Stops & Filters?

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One of the most appealing aspects of quantitative trading is that there is no end to the number of different components that we can add to our strategies in order to improve performance and reduce risk factors. While most traders agree that simpler systems will be more robust over time, we still catch ourselves constantly looking for tweaks to improve our returns.

wasting time

Is experimenting with strategy filters and stops a complete waste of time or is there evidence that supports the effort?

About a month ago, I covered an article about an attempt to salvage a bad strategy. その記事で, we looked at a stock rotation strategy that Cesar Alvarez had written about and how disappointing its performance had been. Despite the poor performance of the base strategy, Cesar received a number of comments from readers suggesting different ways that the strategy could be salvaged using different types of filters and stops.

で a recent update to that post, Cesar tested quite a few of these ideas and discussed the results. His backtesting results demonstrates that despite the large number of ways to combine stops and filters, most of them actually have a negative impact on the base strategy.

The Base Strategy

The base strategy that Cesar was working with ranks each of the stocks in the S&P 500 based on performance over the previous nine months. The strategy holds the top ten stocks and rebalances on the first trading day of each month.

This strategy posted an annual return of 7.3% 差出人 2004 を通じて 2013. The maximum drawdown during that time was 61.01%. This represents a slightly better annual return with slightly large drawdown than simply holding SPY would have produced.

Adding Stops & Filters

Cesar tested six different types of filters that were based on moving averages and new highs. These are the types of filters that are commonly suggested for improving strategies of this type.

意外にも, all but one of the filters actually lowered the annual return. Requiring a stock to be above its 50- or 200-day moving simple moving average made almost no difference in this case because stocks ranking highest in 9 month return tend to already be trading above their moving averages.

Adding stops to the strategy had a similar effect. While the stops did a good job of reducing the maximum drawdown, they also reduced the annual return quite significantly.

The Type of Filter That Did Work

The one type of filter that did work across the board was a market timing filter based on the S&P 500 closing above its 200-day simple moving average. Requiring the S&P 500 to be in an uptrend improved the returns on every version of the strategy that Cesar tested.

What this case study shows us is that it is important to integrate a level of critical thinking when adding filters and stops to our strategies. Filters that replicate the basics of the strategy are probably a waste of time, but there are probably other filters out there that would be worthwhile to experiment with.

The Slight Tweak Than Can Add 2% Annually To Your Rotation Strategy

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As rotation strategies are becoming more and more popular with quantitative traders, creative traders are finding different ways to add value to their strategies. The constantly increasing number of ETFs available for these types of strategies is allowing traders the ability to really focus their capital with extreme precision.

An article that was published by Dorsey Wright Money Management did a nice job of illustrating one such adjustment that focused trading on the momentum stocks in each sector that was considered. That slight tweak ended up adding 2-3% in average annual return to the simple rotation strategy that they were backtesting.

sector rotation

Making a slight adjustment in the ETFs that were considered allowed this strategy to tack on an additional 2% average annual return.

The article gives us just one example of how focusing our capital on specific areas at different times can greatly increase the returns of rotational strategies without a severe impact on the overall risk. It would be interesting to see if there are any strategies that have found success with combining multiple adjustments like this one.

基本的な戦略

The basic strategy that the article broke down held either 3 または 5 of the SPDR Sector ETFs at any given time. The portfolio was rebalanced each month based on either 3 または 6 months returns. That gave them four different version of the same rotation strategy:

  • Holidng 3 ETFs with a 3-month lookback period
  • Holding 3 ETFs with a 3-month lookback period
  • Holding 5 ETFs with a 3-month lookback period
  • Holding 5 ETFs with a 6-month lookback period

Each of these strategies produced very similar results. The highest risk adjusted return was produced by the one that held 3 ETFs with a 3-month lookback period. That strategy produced an average annual return of 11.3%.

Improving The Strategy

The way that the article improved the strategy was to give it the option to trade the standard SPDR ETF or the company’s momentum version of that ETF. This decision was simply based on whichever had the best return over the strategy’s lookback period.

The article points out that there are times when the market will favor large-cap stocks, which means the standard ETFs will be more favorable. There are also times when the market favors smaller-cap momentum stocks, which would favor the momentum based index.

Giving the strategy the option to choose between the standard ETF or the momentum index increased the average annual profit for the 3 株式, 3-month lookback period strategy to 13.5%. Each of the other three versions saw similar jumps. While there were also jumps in standard deviation, the increased profits were substantial enough to offset those increases.

 

The Difference Between Optimization and Curve-Fitting

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Optimization and curve-fitting are two terms that are very common among quantitative traders. They are so common that many traders confuse the terms, or use them as synonyms when they actually have very different meanings.

Michael Harris recently published a guest post on System Trader Success that broke down the meaning of each of these terms and explained how they interact with each other. He also shared a process for determining how likely a strategy was to be exposed to a curve-fitting bias that is based on how its parameters are utilized.

curve-fitting

Knowing the difference between optimization and curve-fitting can help you avoid exposing your strategy to backtesting biases.

Optimization vs. Curve-Fitting

Michael began by defining each of the two terms individually. What this shows us is that they have subtle differences with respect to each other. Here is how he explains it:

As already mentioned, curve fitting may involve optimization but the latter is a process with a much broader scope and includes many more possibilities than curve-fitting.

Michael looks at strategy optimization from the viewpoint of finding the best collection of entry and exit signals for a backtesting period. He explains that curve-fitting focuses more on the results than the signals that caused the result.

Is Curve-Fitting Really The Problem?

Another interesting point that Michael brings up is that there is no mathematical proof that optimized systems are more likely to fail because they are curve-fit. He suggests that it is possible for any optimized strategy to fail at any point, and that the strategy failure has nothing to do with what parameters the system uses.

He explains that a different form of bias is far more likely to cause a failure:

それにもかかわらず, optimization that causes selection of entry and exit collections is in general a problematic process because it introduces survivorship bias.

Michael argues that in almost every case where an optimized strategy fails, サバイバーシップ ・ バイアス is more likely to blame than a curve-fitting bias.

How To Gauge Optimized Trading Strategies

While Michael does not believe that curve-fitting failures are nearly as prevalent as many traders believe, he does discuss how some strategies are more likely to be exposed to curve-fitting than others. In order to gauge how likely an optimized strategy is to be exposed to curve-fitting, Michael divides them into three different classes.

The first class contains strategies where the optimized parameters define both the entry and exit signals. These strategies are the most vulnerable to curve-fitting.

The second class contains strategies where just the entry signals are defined by optimized parameters. These strategies are less likely to be exposed to curve-fitting than those in the first class.

The third class contains strategies where the optimized parameters define only the exit signals. These strategies are the least likely to be exposed to curve-fitting.

 

Four Reasons Why Short Strategies Are More Difficult Than You Think

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Short strategies are endlessly appealing to quantitative traders. The ability to take profits out of the market at times when everyone else is panicking and losing money is the type of thing that almost every trader dreams about.

While many traders acknowledge that there is money to be made on the short side of just about any market, we all know that it isn’t an easy task. Short selling is generally considered to be much more difficult than trading long positions, and there is good reason for that.

short selling

Short strategies have tremendous appeal to quantitative traders, but there are more complicated risks than backtesting results are able to document.

Cesar Alvarez has been trading short strategies for years. His logic explains the appeal:

I love shorting stocks because it is very hard psychologically, because of that, I believe that there is a good edge there.

In a post on his blog last week, Cesar admitted that he is considering discontinuing his short strategy that had been profitable from 2006 を通じて 2012. He explained that four different issues came up over the course of 2013 that his strategy really struggled with. These issues don’t appear to be major hurdles in backtesting results, but they are much more complicated in live trading situations.

Can You Take All of the Signals?

The first concern that Cesar discusses is when brokers are unable to locate shares for you to borrow, which is essential in a short sale. Cesar says that, at times, he has experienced difficulties borrowing shares of extremely liquid stocks for short sales.

あなたが知っているように, failing to take one or more of the trades signaled by a strategy could be the difference between a profitable year and a losing year. There is no way to predict or backtest for this type of issue.

Interest on Borrowed Shares

Another drag on the performance numbers of Cesar’s short strategy has been the interest charged for borrowing shares to sell short. He explains that there is very little consistency in this area. For that reason, it is difficult to adjust your backtesting to account for this variable interest.

Increasing Losses

The third issue that Cesar mentions is that a a position moves further against you, it will become a larger portion of your account. This is exactly the opposite of what happens with losers in long positions.

As the price goes further and further against a short position, more and more capital is required to buy back the stock that was borrowed and sold. Cesar discloses that this took a huge toll on his short strategy in 2013.

Black Swan Gaps

The final complication that Cesar has encountered with his short selling strategy is the large gaps that can occur for any number of reason. These Black Swan movements are completely unpredictable, so there is no way to account for them during backtesting. しかし, when they do show up, they have the power to wipe out an entire account quickly.

 

 

戻り値を改善 & 取引日半による制限のボラティリティ

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定量的な取引システムのためのデータを計算するための最も一般的な方法は、次の日の終値に一日の終値からの価格の変化として一日の行動を定義することです. この方法は、1取引日を含み、全体の24時間を含みます. それは毎日のデータのための最も一般的に使用される定義である理由です.

に掲載された論文 2010 ヨゼフルディによって, クリスチャンL. ドゥーニ, そして、ジェイソン法律がで見つかった定量的な縁があるかもしれないことを指摘 オープン・ツー・クローズの使用またはそれに近いツーオープンデータ 毎日のために代わりに近いツー近いデータの復帰戦略を意味します. 著者は徹底的に価格を開くに因数分解から得られた貴重な情報がある可能性が示唆され、多くの情報源を引用しました.

half day data

価格市場が開いている間に起こる移動し、一夜移動を区別することで、より収益性の高い分野にあなたの戦略を集中する可能性があります.

著者らは、このようにして設定データを変更すると、毎日の取引のために理想的であると考えられ 平均回帰戦略 それは、個別銘柄の大きな価格下落、以下の過剰反応から利益に見えました. 彼らは戦略のこのタイプを使用するオープン・クローズとオープン・ツー・クローズの両方のデータが近いツー近いデータをアウトパフォームすることができることを証明するために設定.

データ

著者をバックテストするために使用されるデータ’ 理論はSを構成する銘柄でした&P 500 インデックス, S&P 400 ミッドキャップ指数, S&P 600 小型株インデックス. バックテスト期間は月の範囲でした 30, 2000 2月を通じ 12, 2010.

取引コストを考慮するために, の要因 0.05% 各取引に充填しました. このコストは、個人投資家がにさらされることになる料金の種類を複製するために意図されました.

戦略

この戦略の目標は、任意の判定期間中に最大の負けストックを利用することです, 以下の期間でバック即時バウンスを期待. 戦略は、それぞれの日に分割された2つの異なるデータ期間に基づいて2つのバージョンに分割されます.

戦略の最初のバージョンは、その決定期間と近いツーオープンなデータを使用しています. その後、, それはオープンで、その期間中に最もパフォーマンスが悪いの株式を購入し、その日の終わりにを介してそれらを保持しています.

戦略の第2のバージョンは、その判定期間としてオープンツー近いデータを使用しています. その後、, それはちょうど近く前に、その期間中に最もパフォーマンスが悪いの株式を購入し、次の日のオープンでそれらを販売しています.

戦略バックテスト

不十分各判定期間後に購入する株式を行う多くの方法を決定するために, S内のすべての株式のためのバックテスト結果を提供した紙&P 600 小型株インデックス. 論文の付録に含まれていた他の2つのインデックスについて同様の結果がありました.

紙は、毎日10種類のグループに株式のすべてを分割しました, 意思決定期間中に彼らのパフォーマンスに基づいて、. 最悪のパフォーマンスグループ内の株式のすべてを購入する生産しました の年間平均リターン 215% 最大ドローダウンと 48% 最初のバージョンのための. 戦略の第2のバージョンは、の年間平均リターンを生成しました 73% 最大ドローダウンと 11%.

あなたが見ることができます。, 第二版, 近いツーオープンからポジションを保持する下の年間リターンを持っています, しかし、多くの 以下のボラティリティ. 著者らは、両方のバージョンが標準に近いツークローズ期間を使用するよりも、より良いリターンを生成していることを述べます.