アルゴリズムと機械の外国為替戦略 | OneStepRemoved

Coppock 曲線 : 取引の成功への直線

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Coppock 曲線, 呼ばれる Coppock インジケーターまたは Trendex 指標, クオンツトレーダーにシンプルでありながら成功した機械的な取引システムを構築するための強固な基盤を提供していますインジケータの種類は、.

以下でより詳細に記載されるように, 私はSで売買シグナルを生成するために、私の機械的な取引システムでCoppock曲線を使用&P 500 または他の流動性の高いインデックス. Coppock曲線はまた、取引iシェアーズETFのとではうまく機能.

Coppock曲線とは何ですか?

Coppock曲線は運動量指標であります. 時間をかけて, 彼らは何度ゼロの下で振動します. Coppockカーブインジケータは、最初に説明しました 1962 エコノミストやトレーダーエドウィンCoppockによって. 実際, それは市場の技術者協会ようにうまく動作します (MTA) 認識博士. 功労賞のあるCoppock 1989.

Spiraling staircase

その値は、株式やインデックスの価格動向の長期的な変化の始まりを示すことにあります, 特に上昇傾向の初めに. このインジケータはまた、先物や外国為替市場の底部に信号を送ることができます, まだ私はそれが信頼性が低いことがわかりました.

あなたは、任意の時間枠にわたって、この指標に基づいて売買シグナルを生成するために、機械的な取引のアルゴリズムをプログラムすることができますが、, 私は、一般的に株式や指数市場の幅広い毎月のチャートで使用. まだ, アクティブなトレーダーは確かに毎日、あるいは時間単位の期間でCoppockカーブを使用することができます.

具体的には, 私は、クマの市場の下部にある「買い」信号を生成するCoppock曲線を使用. このインジケータは、クマの集会や実際の市場底とを区別するために特に良いです.

これは、トレンド追従指標であります, ので、正確に市場の底が表示されません. 代わりに, それは私を示していたときに強いです, 強気の集会を安全に自信を持って取引を十分に定着しています.

すべてのベスト, 私の経験でCoppocks曲線に基づいて、信号からの取引がshakeoutsとwhipsawsにかなり耐性があります. Coppock曲線が遅いです, 彼らは安全です.

Coppock曲線は「喪の期間」の終了を知らせます

背景として、, それは注意することは価値があるという博士が主導し、元のアイデア. Coppock彼の指標を開発するためには、生活の自然のサイクルに基づいていました, 死, そして再び新しい生活に戻る前に、喪.

彼は株式市場の通常の上方行進と思いました (したがって、株価指数) サイクルの「生活」の部分のようでした, もちろん、これはつまり「死」が続きました, 弱気市場の間の価格下落の期間.

Dr. Coppockは、株式市場の「喪」期間の長さを計算する際に特に興味がありました, その後、再び市場「長い」再入力しても安全になります. 論理的に, 喪期間の終了時にこのエントリポイントは次の長期的な上昇トレンドの始まりを表すことになります.

作り話の話は、彼が地元の聖公会で司教に尋ねたことを言います, 彼の投資のいずれかのクライアント, 通常bereavements後に喪に費やさどのくらいの人々. 彼は人間の喪が典型的に必要であることを言われました 11 宛先 14 ヶ月, ので、これらは、株価が再び上昇し始めるとき、彼は彼の元の方程式に採択された値を決定することでした.

Coppockカーブは最初毎月のチャートに基づいて長期的な指標として使用しました. もちろんです, 毎月の時間枠で生成された信号はかなりまれです. まだ, 私は市場の様々な取引をCoppockカーブを使用しているため, 私は、売買シグナルの多くを受け取ります.

特に, 毎月の時間枠は、株式やインデックス取引のため、非常に信頼性が高いです. 研究があることを示しました, 以来、 1920 米中. 株式市場, Coppock曲線は、約で勝利信号を生成しています 80% 周波数.

この頃, 現代の市場の急速な売上高と, それは取引サイクルが速くなってきているようです. 毎月の時間枠に加えて、, 一部のトレーダーは、毎日の時間枠が成功Coppockカーブ信号を生成する際に非常にうまく機能することを発見しました.

トレーダーは、毎日または毎時時間枠に基づいて認識し、信号に対応するための機械的な取引システムをプログラムすることができます, 追加のアルゴ取引パラメータは放漫経営の機会を減らすために追加する必要がありますが、.

あなたは、より短い時間枠で信号を生成するCoppock曲線を使用する場合, あなたの特定の市場のためのメイクセンス」喪の期間」のさまざまな方法を使って、あなたの機械的な取引システムを実験でした.

Coppock曲線を計算する方法

Coppockインジケータは、三つの変数に基づいています: 変化の短期率 (ROCと略記), やや長期ROC. Coppock曲線は、加重移動平均を使用して開発されています (WMA) 特定の市場指数の選択された時間帯由来.

言葉で述べた古典的な式:

Coppock曲線は、14期間ROCプラス11-期間ROCの10周期WMAを=

または, プログラミングのための式として、:

Coppock曲線= WMA[10] の (ROC[14] + ROC[11])

ときROC = [(閉じる - 閉じるN周期前) / (閉じるN周期前)] * 100

nが時間期間の数であります.

古典的なシナリオでは, 11 と 14 期間. 別々のROC計算を行うようにしてください.

あなたが見ることができます。, 基本的なセットアップは非常に簡単です - 移動に基づき, 私は与えられたインデックスの変化の割合を計算するために、私の機械的な取引システムをプログラム (Sを言います&PまたはDJIA) 14ヶ月前から.

その後、, 私の機械的な取引プログラムは、11ヶ月前から同じインデックスの変化率を算出し、. 次, 機械的な取引システムは、2つの異なるパーセント変化を加算. その後、, それは上記の合計の10-期間加重移動平均を算出し、.

それはあなたがROCの計算とWMAの計算に異なる期間を使用できることに注意することが重要です. 私は時々古典を使用するために私の機械的な取引システムをプログラム 11- ROCおよび14ヶ月の期間古典10ヶ月の期間よりも短いWMAの時間期間を使用している間.

だから, 私は、多くの場合、2ヶ月または3ヶ月のWMAを使用して使用します (代わりに 10 ヶ月) ROCを使用して計算されている間 11- そして、14ヶ月の価格.

または, あなたは、計算の一部またはすべてのためのより短い時間を使用することがあなたの機械的な取引システムを変更することができます, すなわち. 毎日使用したり、毎時価格の代わりに毎月の価格チャート. これは、複数の信号を生成します, あなたは追加のフィルタを追加しない限り、私の経験では、彼らは信頼性が低いです, 以下に説明するように.

同様, あなたがあなた自身のニーズに合わせて追加の装飾を追加することができます. 任意のイベントで, 一般的な方法は同じまま. 基本的な入力をグラフ化すると, あなたは、出力がかなり滑らかな円弧であることがわかります, この指標の名前の由来.

任意のイベントで, 機械的な取引システムをプログラムするための古典的なCoppock曲線の方程式は次のように述べることができます: 変更の14ヶ月のレートと変化の11ヶ月の率の合計, 10ヶ月の加重移動平均を適用することによって平滑化と.

Coppock曲線「買い」信号

Coppock曲線に, ゼロラインはトリガであります. 価格ラインは下から上昇すると 0 ラインは、低リスクの購買機会を知らせます. Coppockインジケータが最初に以下のとき私の機械的な取引システムは、購入を実行 0, その後、トラフから上方に向かいます.

これは強気の指標として最も効果的であるので、, 私は反対を無視 ("売って") 信号. まだ, 一部のトレーダー, 短い時間枠を使用して、特に, 売り信号を生成し、ロング・ポジションを閉じて取引を実行するためにアルゴ取引システムでCoppock曲線を使用. Active traders can both close long trades and open shorts when the Coppock Curve crosses below the zero line.

The figure below shows the classic Coppock Curve trading strategy using monthly time periods. The buy signal came in 1991. The sell signal came ten years later, で 2001. Note that this long time frame helped me avoid the slump in late 2001 と 2002.

The next buy signal came in 2003 and the sell signal was in 2008. This helped me escape the slump in 2008 and into 2009. Note, also that the current “buy” position, signaled in early 2010, continues to remain open, at least through the date of this chart.

Coppock Curve on S&P 500 monthly chart

The Coppock Curve on an S&P 500 monthly chart

次, for more-active traders here’s a screenshot showing the strategy applied with shorter time periods, as shown on a daily S&P 500 グラフ. もちろんです, many more signals are generated, although in general they are less likely to be winners.

Coppock Curve on a daily S&P 500 グラフ

Coppock Curve on a daily S&P 500 グラフ

重要なこと, the longer the time period, the safer the buy signal. Since my mechanical trading system based on Coppock indicators is a trend-following system, I don’t necessarily capture the immediate gains from the exact moment of a trend reversal. 代わりに, my mechanical trading system gets me “long” just before the beginning of a profitable advance in a bull market.

Adjusting and filtering signals from Coppock Curves

I’ve found Coppock Curves to be highly reliable when used for monthly time periods. 私の経験で, using weekly, daily or hourly time periods usually means that my entries and exits aren’t as “tight” as I would like, meaning that I don’t capture all the gains I had hoped for, and I also have more losses.

しかし, active traders can decrease the ROC variables, which has the effect of increasing the speed of fluctuation in Coppock Curves and will therefore generate more trading signals. もちろんです, even though monthly time periods are my favorite, an ultra-long-term trader could also increase the ROC time periods to slow fluctuations even more, thus generating fewer signals.

上記述べたよう, in order to receive earlier entry signals, I usually decrease the WMA downward from 10 ヶ月, sometimes to 6 ヶ月, and often to as little as 2 ヶ月. By programming my mechanical trading system carefully with just the right WMA period, and filtering the signals, I maximize my profitability in a given market.

If you want to use Coppock indicators for active trading, I recommend that you filter the trade signals generated by your mechanical trading system so that you only accept trades which are in the same direction as the current dominant trend. You’ll find this mechanical trading strategy to be the most profitable, since you can avoid many losing trades by filtering the signals.

Which markets show reliable Coppock Curves?

I use my Coppock curve-powered mechanical trading system to trade a range of indexes, especially those based directly on stocks, など、:

  • Dow Jones Industrial Average
  • S&P 500
  • NASDAQ Composite
  • EURO STOXX 50
  • FTSE 100
  • Nikkei 225
  • Hang Seng

同様, if you’re focused on ETFs you’ll find that a mechanical trading system using Coppock Curves will allow you to catch the beginning of trends in specific market niches, such as biotechnology, energy, and international or regional equities niches.

The key is to make sure you trade only the liquid indexes. それ以外の場合, you may run the risk of being shaken out during “fake” trend changes.

Trading Coppock Curves in non-equity indexes

同様, for the sake of diversification and to avoid issues with correlation, I also program my mechanical trading system to spot and trade Coppock Curves in non-equity indexes as well. もう一度, I focus on markets which have sufficient liquidity.

There are some profitable non-equity indexes, including iShares and ETFs, which can be traded using Coppock indicators:

  • Bloomberg US Treasury Bond Index
  • Bloomberg Canada Sovereign Bond Index
  • Bloomberg U.K. Sovereign Bond Index
  • Bloomberg US Corporate Bond Index
  • Bloomberg GBP Investment Grade European Corporate Bond Index
  • ブルームバーグユーロ投資適格の欧州コーポレート・ボンド・インデックス
  • ブルームバーグ円投資適格コーポレート・ボンド・インデックス
  • iシェアーズバークレイズ 7-10 年財務省債券ファンド
  • iシェアーズバークレイズ 20 年財務省債券ファンド
  • シュワブ短期米. 財務省ETF
  • バンガード短期国債ETF
  • PIMCO 1-3 年米. 財務省インデックスETF

すべての上に挙げた以外の株式インデックスを取引するとき、私はCoppock曲線から信頼性の高い信号を見てきました. いつものように, キーは非常に液体でのみこれらの市場における機械的取引システムを使用することです, そうアルゴリズムが確認された信号は、それを取引する前に正当であることを合理的に確信していること.

Coppock曲線は、成功への直線を示します

近年では, Coppock曲線は、この実証済みトレーディングツールに再び目を向けているトレーダーから新たな関心を描画されています. 見る, たとえば, これらの最近の金融プレスでCoppock指標の言及します: Jay On The Markets, と、 follow up article, as well as in various trader musings.

要約すると, I can say that Coppock Curves can lead you straight to success, as long as you have the patience to let your mechanical trading system do the work for you. If you use the length of variables’ time periods which are most appropriate for your chosen markets, you should do very well with Coppock Curves.

Backtesting Biases and Variations

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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.

Backtesting bias in VTI

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%.

Backtesting bias for xly

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.

累積RSIシステムのための出口戦略の比較

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This is the third post in a series covering the work Larry Connors and Cesar Alvarez have done using the 2-period RSI as an entry signal. In the first post, we discussed their evidence that shows how accurate the indicator can be in identifying short term oversold situations. その後、, we reviewed how they took that entry signal and built the 累積 RSI システム around it.

In the second post, I noted that Connors and Alvarez had suggested that there were a number of different exit strategies that could be implemented. In a later chapter of their book, 短期的な取引戦略その作業, they discussed five different types of exits and then provided data from backtesting some of those signals.

exit strategies

Five Different Types of Exit Strategies

Much like using the 2-Period RSI as an oversold indicator, many of these exit strategies go against what has become my natural preference towards long-term trend following strategies. Most long-term trend following strategies look to hold on to positions that are closing up, making new highs, and closing above their moving averages.

It is important to remember that we are looking at these strategies from a very short-term viewpoint. That explains why they can be almost exactly opposite from some of the long-term trend following strategies that I prefer and still be profitable.

Fixed Time Exit Strategies

Fixed Time Exit Strategies are exactly what the name implies. They commit to exiting a position a certain amount of time after the entry. If you recall, the average holding time for a position using the Cumulative RSI Strategy was between three and four days. Based on that, it is reasonable to assume that if a position is going to produce a positive return, it will do so sooner rather than later.

First Up Close Exit Strategies

First Up Close Exit Strategies look to exit a position on the first positive close made after a position is entered. 明らかに, this only works when used with a short-term system that is looking to take quick, small profits out of the market with a very high win rate. In those situations, it can be surprisingly profitable.

New High Exit Strategies

New High Exit Strategies exit positions after they close at a new high. As I said, this concept runs counter to the long-term trend following approach, but can be very profitable in short-term situations. These strategies wouldn’t work if you were buying at new highs, but since the Cumulative RSI System looks to enter markets that have become oversold during uptrends, a bounce back up to new highs would represent a profitable situation.

Close Above the Moving Average Exit Strategies

Close Above the Moving Average Exit Strategies provide exit signals when a market closes above a specified moving average. The logic here is very similar to the New High Exit Strategies. When entering a position, an oversold market in a long-term uptrend will likely be below its moving averages, so a bounce back above those moving averages would represent a profitable trade.

2-Period RSI Exit Strategies

This is the exit strategy that was used in backtesting the Cumulative RSI System. It looks to exit a position when the 2-Period RSI closes above a certain number. Connors and Alvarez suggest values of 65, 70, または 75 for this number. The concept behind these strategies is that once the 2-Period RSI value has risen to one of those values, the market is no longer oversold and may actually have become overbought.

Backtesting These Exit Strategies

While they could have simply stopped after identifying all of these different strategies, what I like about Connors and Alarez’s work is that they went a step further and actually tested three of these strategies. In order to do that, they looked at every stock from 1995 を通じて 2007 that traded above its 200-day moving average and had closed at a 10-day low. This provided them with 63,101 エントリ信号, so this was certainly not a small sample size.

Fixed Time Exit Strategies

On those entry signals, Fixed Time Exit Strategies performed the worst of the three strategies tested. しかし, they still performed much better than I expected. Exiting after holding for one day produced an average trade return of 0.61%. Increasing the hold time to just three days jumped that return number to 1.76%. Continuing that trend, increasing the hold time to 5 days provided a return of 1.97%, and holding the position for 7 days produced an average return of 2.05%.

Close Above the Moving Average Exit Strategies

While the Fixed Time Exit Strategies produced impressive return numbers, the exit strategies based on moving averages performed even better. Exiting on a close above the 5-day moving average produced an average return of 2.65%. Using the 10-day moving average increased the average return to 2.80%.

2-Period RSI Exit Strategies

Much like we saw with using the 2-Period RSI as an entry signal, the higher RSI values returned more profitable trades on average. Using a 2-Period RSI value of 65 produced an average return of 2.76%. Increasing the RSI value to 70 gave us an average return of 2.83%, and increasing the RSI value even higher to 75 gave us an average return of 2.93%.

Choosing an Exit Strategy

While I was not surprised that the dynamic exit strategies outperformed the Fixed Time Exit Strategies, I was surprised at how well those fixed time strategies performed to begin with. It appears that choosing an exit strategy for your system has more to do with your comfort level with a given strategy than its actual performance.

While using a value of 75 for your 2-Period RSI Exit may return a higher average profit than using a value of 65, if you lose sleep worrying about positions that don’t make it to that higher value then you might be better off using the lower value.

Why You Must Design Your Own Trading System

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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.

取引システムのドローダウンと感情

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What do baseball and trading system drawdowns have in common? A lot more than I ever realized.

If you haven’t read the book or seen the movie Moneyball (which I highly recommend), professional baseball adopted a statistical approach to recruitment and gameplay around 2000.

The Oakland Athletics adopted the approach before any team. They experienced tremendous success in their first system-driven season, despite countless expert predictions of imminent failure.

I started reading The Signal and the Noise by Nate Silver last night. The baseball chapter picks up where Moneyball left off. Dustin Pedroia, a star second baseman for the Boston Red Sox, enters the scene as an unlikely success story.

Most scouts overlooked him. He is short. He has a paunch and bowed legs. Nobody looks at the guy and thinks “professional athlete”.

Dustin Pedroia held strong through a trading system drawdown of his own

Dustin Pedroia held strong through a trading system drawdown of his own – an unexpectedly low batting average.

Sticking with the system

Pedroia batted a lackluster .198 when the Red Sox brought him to the major leagues in August of 2006. The first month of the 2007 season started off even worse at .172.

 

A team like the Cubs, who until recently were notorious for their haphazard decision-making process, might have cut Pedroia at this point. For many clubs, every action is met by an equal and opposite overreaction. The Red Sox, 一方、, are disciplined by their more systematic approach. And when the Red Sox looked at Pedroia at that point in the season, James told me, they actually saw a lot to like. Pedroia was making plenty of contact with the baseball – it just hadn’t been falling for hits. The numbers, most likely, would start to trend his way.

“We all have moments of losing confidence in the data,” James told me. “You probably know this, but if you look back at the previous year, when Dustin hit .180 or something, if you go back and look at his swing-and-miss percentage, it was maybe about 8 パーセント, maybe 9 パーセント. It was the same during that period in the spring when he was struggling. It was always logically apparent – when you swing as hard as he does, there’s no way in the world that you make that much contact and keep hitting .180.”1

Trading System Drawdown

Every trader goes through a slump – even the best of them. When your trading system drawdown reaches uncomfortable levels, how do you cope?

I had the opportunity to lead managed fund sales at one of the largest forex brokerages in the world. The product was the Sentiment Fund and it was AWESOME. When the fund took off and upper management suddenly got involved, the fund had $40 million under management and nearly 1,000 investors.

If there’s one thing that you can count on with forex traders, it’s that they are going to lose. All the time. No doubt about it.

The firm received real time information on the positions of all clients. Whenever too many traders held positions long or short, the fund did the exact opposite.

それはそれ. 、 automated strategy was stupid simple, but the returns were amazing. The aggressive fund kept the leverage at 2:1, yet was on track to earn 40% 毎年.

Bad luck

Two months before the hand off, the fund experienced a sharp drawdown of -8%. Like most people, investors thought of 40% returns as 40/12 = 3.3% profit per month. They don’t think about the natural slumps that happen – just like the one Pedroia experienced.

こと “surprise” drawdown caused a mass herding effect. It didn’t matter that the fundamentals of the strategy were unchanged. Forex traders didn’t wake up smart last month and know how to game the system. Bad luck happens to the best strategies. それにもかかわらず, investors ran for the exits from a world class strategy.

Were the guys on the systems desk worried about the strategy that they built? No way! When Pedroia went through his slump, he could have overreacted and start messing with his batting stance. He could have adjusted his distance to the plate or any number of things.

Pedroia knew that his system was good. The systems guys knew their strategy was phenomenal. In spite of all the pressure, they held firm and blew past the drawdown by the time I handed off the sales effort.

What element of trading matches the “swing-and-miss percentage”? The Red Sox clearly used it to justify sticking with their system. In Sentiment Fund’s case, it was knowledge of the underlying system.

How can quantitative traders with less obvious fundamental theories know that their system is sound? I’d love to hear your comments.

1. Excerpted from The Signal and the Noise by Nate Silver, ページ 104.

システムの期待

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I found myself outside today looking for an excuse to make a video. It’s 80°F / 27°C, sunny skies and a soft breeze in Dallas. The last place anyone wants to be is in front of the computer when the weather is this nice.

No doubt that some active daytraders or people that hate their jobs are thinking the same thing. I suspect that the motivation for most people making automated expert advisors is the dream of making money without doing anything. Turn on the software and wait for the trading profits to roll in. That was certainly the case with the company Forex Made Sleazy… 私が言いたいのは, Forex Made Easy several years ago.

We do have a handful of customers that trade profitably, but even then, it takes a long time for an automated system to get to the point where it’s largely hands off. The best conceived ideas, which I would define as plausibly worthy of my own investment funds, takes a bare minimum of several months to execute from start to finish. This also presumes the unlikely notion that the idea has genuine potential to start with.

Even the most simple, valid concepts encounter substantial setbacks before the system can truly run hands-free. It’s usually not some kind of epic programming disaster where the client wants black and the programmer makes white. 誤解しないでください。; communication is critical. The smoothest projects are always the ones where both parties understand one another readily.

それにもかかわらず, even the most well-oiled team experiences countless hiccups in the process of morphing from idea to reality. Simple ideas often fall the most vulnerable to real world problems. Trade execution stands out as the most common obstacle. If anything goes remotely unexpected, a potentially profitable scenario may lead to unexpected losses.

I worked with one client that came up with a simple idea that mathematically showed a heavy positive expectation. Yet when we launched the idea in the real world, the prices that the system absolutely required in order to function never came through. Slippage occurred precisely when it was the most damaging.

We had to go back to the drawing board looking for ways to re-engineer the expert advisor where the importance of execution declined. That setback alone took several months to overcome in any meaningful sense.

The take away here is that it’s totally unreasonable to expect to hire a forex programmer and expect a dramatic shift in profits and life style. The best ideas take several months before they are worthy of running their full account balance. 残念なことに, most of the ideas out there are not good to begin with. That’s why making an EA that is profitable over the long run is so incredibly difficult.