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Does SKEW Predict VIX?

12 月 22, 2017 によって ショーンオバートン 4 コメント

SKEW should lead VIX, 右? Traders get worried about a crash, which might anticipate volatility in the S&P 500.

SKEW is in green.
ビクス is in blue.

場合 SKEW was a perfect predictor of ビクス, then you’d expect the blue line to look like the green line with a small gap in between them.

skew against vix

The theory was that SKEW (in green) would pull up VI (青で).

 

A quick scan of the chart shows that’s not the case. There are occasions where green spikes up followed by blue, but it intuitively feels to me like a case of cherry picking. また, notice the largest blue spike around value 500. 場合 SKEW lagged ビクス of the other way around.

Just for the sake of being thorough, I measured the cross correlations of If SKEW と ビクス using both my smoothed values and the unprocessed ones.

Here’s the cross correlation of the smoothed values.

Cross correlation of skew and vix

The cross correlation of smoothed SKEW and VIX

 

And here’s the cross correlation of the unsmoothed values.

Cross correlation of SKEW and VIX

If anything, the hypothesis is backwards. SKEW 18 days ahead of ビクス has a -19% 相関関係. The correlation should be positive and > 40% to carry any substantial meaning. The weak correlation value and the fact that it’s negative that this idea is better tossed in the bin.

ここをクリックしてください。 to download the data used in this analysis. You’ll noticed that I first normalized the ビクス と SKEW values to allow for easier visual comparisions. Because the data is extremely noisy, I applied a 7 day SMA to make visual comparisons easier.

The data used was from October 16, 2013 12月に 21, 2017.

何です SKEW?

SKEW, which is another volatility index run by the CBOE, provides a measure of how worried traders are about tail risks.

Here’s the full description directly from the exchange:

The crash of October 1987 sensitized investors to the potential for stock market crashes and forever changed their view of S&P 500® 返します. Investors now realize that S&P 500 テイル ・ リスク – the risk of outlier returns two or more standard deviations below the mean – is significantly greater than under a lognormal distribution. The Cboe SKEW Index (“SKEW”) is an index derived from the price of S&P 500 テイル ・ リスク. Similar to VIX®, the price of S&P 500 tail risk is calculated from the prices of S&P 500 out-of-the-money options.

SKEW typically ranges from 100 宛先 150. A SKEW value of 100 means that the perceived distribution of S&P 500 log-returns is normal, and the probability of outlier returns is therefore negligible. As SKEW rises above 100, the left tail of the S&P 500 distribution acquires more weight, and the probabilities of outlier returns become more significant. One can estimate these probabilities from the value of SKEW. Since an increase in perceived tail risk increases the relative demand for low strike puts, increases in SKEW also correspond to an overall steepening of the curve of implied volatilities, familiar to option traders as the “スキュー”.

何です ビクス?

This is also taken directly from the exchange:

The Cboe Volatility Index® (ビクス® インデックス) is considered by many to be the world’s premier barometer of equity market volatility. The VIX Index is based on real-time prices of options on the S&P 500® インデックス (SPX) and is designed to reflect investors’ consensus view of future (30-日) expected stock market volatility. The VIX Index is often referred to as the market’s “fear gauge”.

The VIX Index is the centerpiece of Cboe Global Markets’ volatility franchise, which includes volatility indexes on broad-based stock indexes, exchange traded funds, individual stocks, commodities and several strategy and performance based indexes, as well as tradable volatility contracts, such as VIX options and futures.

These revolutionary volatility products can offer investors effective ways to help manage risk, leverage volatility and diversify a portfolio.

以下の下でファイルさ: 戦略の取引のアイデア タグが付いて: CBOE, スキュー, ビクス

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

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

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.

以下の下でファイルさ: ルール タグが付いて: カーブフィッティング, ドローダウン, 専門家アドバイザー, 資産配分, スキュー

One line of code makes all the difference

2 月 9, 2017 によって ショーンオバートン 4 コメント

I was really excited about my Pilum strategy two months ago. The research looked great and everything was ready to rock and roll. Demo testing began and then… not much happened.

The Quantilator is (mostly) finished, which finally gave me time to circle back and review what happened with Pilum.

Live demo trading of Pilum

Live demo trading of Pilum. 12月 9, 2016 to Feb 7, 2017

The expected outcome was that I would win 75% 時間の. Trades were infrequent, so I thought maybe I’m just having bad luck. But then my win rate remained stuck around 50%. Simple statistical tests told me this was unlikely to be bad luck.

I used the research time to pour over my research code and to compare it with live trades. What I found was that a single line of code (AHHHHHHHHHHHHHHH!) was incorrectly calculating my entry price, dramatically overstating the profits.

、 flawed code produced this equity curve from a single combination of settings:
Flawed Pilum backtest

When the actual, correct result looks like this with those same settings:

The accurate backtest of Pilum

The accurate backtest of Pilum

I’ll be honest… I like the flawed backtest a lot more!

The new, single-setting backtest isn’t as good, but it’s still trade-worthy. There are some characteristics that I dislike and features that I love. Let’s dig into those.

What I dislike

The frequency of trades is very low. Out of 19 months there were a total of 43 取引. 43 trades to comprise a backtest on 40+ instruments is a very small number.

If it weren’t for the statistical pattern backing up the frequency, I would not consider the test. しかし, there are 20,000 bars each on the 44 instruments. あります。 880,000 total bars used to analyze whether my Pilum pattern offers any predictive value.

The most valuable predictions, しかし, are also exceptionally rare. That’s why I’m not able to get the trading frequency higher, which would potentially smooth the returns.

What I love

My previous systems like QB Pro and Dominari traded actively for relatively small wins. Trading costs exercised a massive impact on the overall performance.

The accurate backtest of Pilum

The accurate backtest of Pilum

Now look again at the correct equity curve (the image to the right). Do you see the final profit of roughly 0.14? That’s a 14% unleveraged return over a 19 ヶ月の期間.

Allocating 2:1 または 3:1 leverage on this strategy could average annual returns of 15-25%.

Detecting hidden risk

A key measure of risk is skewness. You may not use that term yourself, but it’s something most of you already understand. The biggest complaint about people trading Dominari was that the average winner relative to the average loser was heavily skewed towards the losers.

Dominari wins on most months, but when it lost in December it was devastating. I implemented what I thought was a portfolio stop after the December 9th aftermath. Then I had a smaller, but still very painful, loss in January. The portfolio level stop loss of 3% should prevent future blowouts now that I know what goes wrong.

I still believe in Dominari. しかし, I obviously lost the work of most of the year due to those events.

Knowing that skewness is a good measure of blowout risk (even if you’ve never seen it in a backtest, like happened with Dominari), Pilum looks extremely encouraging.

This is a histogram of profit and loss by days. You should notice a few things.

The tallest bar is to the right of 0. That means that the most frequent outcome is winning.

worst and best days

The biggest winning day is dramatically better than the worst losing day. The worst outcome was a loss of 2%. The best outcome is gains near 10% in a single day (unleveraged!).

This is the statistical profile of an idea that’s much more likely to grab an avalanche of profits than it is to get blown out.

It gets even better

low correlation

Would you say that the blue and red equity curves are highly or loosely correlated? Look closely.

Writing this blog post made me think carefully about the Pilum strategy. I decided that maybe I should see if all of the profits are coming from different settings at the same time. There’s very little risk of overfitting the data as my strategy only has 1 degree of freedom.

The blue bars are the equity curve of Setting 1.

The red bars are for Setting 2.

Do you think these are tightly or loosely correlated?

If you said loosely correlated, then you are correct. Notice how each equity curve shows large jumps of profit. Did you notice how those profit jumps occur on different days?

The blue setting skyrockets on a single day in November 2016. It leaves the red equity curve choking in its dust.

But then, look what happens as I advance into December. The red curve dramatically catches up to the blue curve and even overtakes it.

The correlation between the 2 strategies is only 57%.

Combine multiple settings into 1 portfolio

Combined settings Pilum equity curve

This is a much nicer equity curve!

Loose correlations are a GIFT. Combining two bumpy equity curves into a single strategy makes the performance much, much smoother.

The percentages of days that are profitable also increases. Setting 1 is profitable on 58.0% of days. Setting 2 is profitable on 53.5% of days.

しかし… combining them makes Pilum profitable on 68.2% of days. 素晴らしい!

That also provides more data, which puts me in a stronger position to analyze the strategy’s skewness. Look at the frequency histograms below. They’re the same type of histograms that I showed you in the first section of this blog post. As you’ll notice, they look a lot different.

Pilum most probable daily profit and loss

The most probable outcome for any given day is a small winner

The tall green bar is the most probable trading outcome for any given day with filled orders. The average day is a positive return of 0-1%.

The small red bar is the worst trading day of the combined strategy.

The small green bars are the best trading days of the combined strategy.

Look how far to the right the green bars go. The largest winner is more than 3x the biggest loss. と, there are so many more large winners compared to losers.

Giant winners are far more likely than comparable losses.

The Plan

I immediately pushed Pilum into live trading this combination of two strategies. I expect that adding a second degree of freedom and running about 30 different versions of the strategy – all with different settings – will add to the performance and smooth the returns even further.

Dominari hasn’t been working on my FXCM account, which is very difficult to accept because the lacking performance seems to be a buried execution issue. Pilum, しかし, trades very infrequently. It’s unlikely that execution quality will make a dramatic difference in the long term outcomes.

だから, I’m going to convert the FXCM account to trading Pilum exclusively. That will be offered as a strategy on Collective2 within the next few weeks, a company with whom I’ve been working closely. Their users are more investor rather than trading oriented – they’re far more likely to view low trading frequency as a good thing. I suspect that most people here have a different opinion and want to see a lot of market action.

I’ll write an update on Dominari shortly.

以下の下でファイルさ: Pilum, 戦略の取引のアイデア タグが付いて: 相関関係, カーブフィッティング, 自由度, ルール, equity curve, 周波数, FXCM, histogram, レバレッジ, QB プロ, リスク, スキュー, 統計情報

Dominariに大きな変化

3 月 9, 2016 によって ショーンオバートン 24 コメント

私はそれを言いました ここで と ここで と ここで. 私Dominariとの最大の問題は、取引コストであります. 物事は、私は2つのいずれかの操作を行いまで本当に離陸するつもりはありません.

  1. 取引コストを削減
  2. 各取引に多くのお金を作ります

私は去年の9月か10月頃からDominariに取り組んできました. 数ヶ月のために私の脳をラッキングした後、, 私は多かれ少なかれ取引の収益性を改善するアイデアをオフに書きました.

市場が閉じた後にそれが突然金曜日に先週変更しました. 自分のシステムが住んでトレードするための最良の理由は、不採算力創造の苦悩. 気持ちは私にデイモンド・ジョンのの多くを連想させます (シャークタンクから男) 新刊 ブロークのパワー. 人生はあなたの道を進んでされていない場合, それはトップになるために最善のことができます誰が機知と創造的です.

誰も破っや極度のストレス下で感じたいとは思いません. 限り、我々はそれらの感情を憎むように, 彼らは多くの場合、パフォーマンスの最強ドライバーです. それは私がDominariで、今どのように感じます. 私はそこに得るために非常に近いですし、その不足している成分を修正する方法がわかりませんでした.

そのストレスがなかったら, 私は先週の金曜日、私のシンプルでありながら非常に強力な洞察力を持っていないだろう.

そして、笑わないでください. 変化はとてもダム、あなたは私と一緒に間違っているのだろうかしようとしていることは明らかです. あなたは、システムの設計の厚いにいるとき, 醜い真実は時々あなたが雑草で迷子ということです. または他の植物学のメタファーを使用するには, あなただけの森の代わりに木を参照してください。.

私の主要な洞察力はわずか指値注文を使用するには、出口戦略を変更することでした, 以前のに対し、私はバーの近くに基づいて終了しました. 私はポイントが最終的に沈んだことを最終的に十分に頭の上に私を打つ2繰り返し行動に気づきました.

私の貿易が最適な位置に閉鎖機会の数は大幅にテーブルの上に残された金額を上回るように見えました. 私のための重要な洞察力はどこに最適なその指値注文を配置する場所を実現しました。. そして、あなたのそれらのための私のニュースレターに, 密接に関連して起こります 自動利益を取ります 私はすべての週の話を​​してきたこと.

バックテストの前提条件と結果

backtestsをしている私の運転マントラは、仮定の数を最小限にすることです. 小売トレーダーのためのスプレッドはより劇的に変化しています 2008 今日へ. GBPCHF上の私たちの典型的なスプレッドのようなものだったとき、私はFXCMでブローカーとして働いて覚えて 8-9 ピップ. 私は今、日常のようなものを支払います 2 ピップ. それは偶然に推測することなく、途中で何が起こったかをモデル化することは不可能です.

私はそれがはるか​​に説得力のある生信号を分析するために見つけます, 両方の歴史と最近の市場のデータについて, その後、取引コストは、今日の市場で有利である可能性があるかどうかを解釈します. “生信号” 理想的な信号であります, 完璧な実行を前提として1, 何も滑りません, ロールオーバーはありません, スプレッド、ノーコミッションありません. 当然の結果では、過去の実績を誇張しているということです, しかし、利点は、核となるアイデアは、合理的なリスクで市場を予測することができるシステムであるかどうかを非常に明確な考えを持っているということです.

ポートフォリオで使用される全レバレッジがあります 7:1. 私が持っている場合 $50,000 トレーディング勘定とポートフォリオ内のすべての通貨ペアでポジションを開催しました, その後、これらの取引の想定元本は等しくなります $350,000 (50へ * 7).

もう一つ重要な点は、私がの固定位置・サイズを使用することです $12,500 1 トレード当たり. 貿易のサイズが増加しないか、バックテストの間に減少しません, 私はお金の管理の変数を追加することなく生の信号の影響を分離することを可能にします.

ここでは、バージョンと私の貿易指標です 1 ルールの. フルサイズで表示するには画像をクリック.

バージョン 1 backtest of Dominari

Dominariの最初のバージョンは、の利益率を持っていました 1.26.

ここで後Dominariバージョンの変更です 2.0.

Dominariの私の新しいバージョンがに利益率を増加させます 1.59 有意に低いドローダウンで.

Dominariの私の新しいバージョンがに利益率を増加させます 1.59 有意に低いドローダウンで.

私の最良のシナリオは、利益率が他のジャンプになることを期待することでした 10 ポイントまたはその近傍, 多分に利益率を伸ばします 1.35 またはその辺. それは二重よりも損益分岐より上にエッジを見ることが信じられないほどエキサイティングです (行くから $0.26 にエッジ $0.59 セントエッジ).

私がについて最も興奮するとリターンのスキューです. ほとんどの平均回帰システムは、エッジを探したが負けトレードの影響に圧倒されています. それはバージョンであった場合 1.

Skew of Dominari version 1

最大の敗者はバージョンで最大の勝者を上回ります 1.

Dominariのこの新しいバージョンでは、非常に最初のものです 平均回帰 私が今までに受賞した尾開発した戦略 (すなわち, 最大の勝者) ほぼ失う尾を等しく (最大の敗者). それはほとんど常に、平均回帰戦略と反対です. 別の方法は言いました, 極端な転帰のリスクプロファイルが大幅バージョンで改善しました 2.

Fat tails in Dominari v2

最大の勝者の影響は、バージョンの最大の敗者とほぼ同じです 2.

そして、ほとんどのトレーダーが最も気にすることをメトリック, ドローダウン, 乱暴に改善されています. バージョン 1 のドローダウンを示しました。 5.72%. 新バージョンでは、その割合であります 1.77%.

Out of sample backtest for Dominari version 2

サンプル性能のうち、サンプルの性能とほぼ同じです, 大幅に異なる市場の状況にもかかわらず、.

私は最近のデータにサンプルのうち、私のテストを歩いていると, カバーします 2013-2015, バージョンのパフォーマンス特性 2 で、サンプルテストとほぼ同じです. 利益率は、同一でした 1.59, そして最大ドローダウンはありました 2.01% ため 2013-2015.

期待される性能パラメータに理論を翻訳

もう一度, これらのメトリックは、上記の完全な実行となし取引コストの理想的な世界であります. 現実世界の性能が低いリターンと高いドローダウンを持っています. ライブ取引データを有することに利点が、私は今、私の予想貿易精度と利益率のインテリジェント推定値のいくつかの種類を作ることができるということです. どれだけ誇張がある可能性が高い理想化されたリターンです?

私は現実の世界で期待利益率を計算するために通過したプロセスであります 5 ステップのプロセス. 私は英会話のステップを書き出すしようとした場合、何の意味を作るつもりはないと思います. 代わりに, 私はあなたが新しい戦略に期待される性能にライブ取引データを外挿する方法については、ステップのプロセスによってステップを表示することができ、スプレッドシートを共有することを選択しました. ここをクリックしてください。 スプレッドシートを表示します.

私のライブ取引の予想収益率は間であることが期待されます 1.29 宛先 1.39. からジャンプする必要があり、ライブ取引のための期待%の精度 62.55% 宛先 70.8%.

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以下の下でファイルさ: ルール, あなたの概念を歴史的にテストします。 タグが付いて: バックテスト, 脂肪のしっぽ, GBPCHF, レバレッジ, 平均回帰, プロフィットファクター, スキュー

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