Mean reversion strategy pdf

The following article is sponsored by the Dr. Stoxx Options Letter.
In my two previous articles on this topic, I detailed how traders and investors use one of the most common components of technical analysis, the simple moving average. A moving average is a running average of the closing price of a stock over x number of time periods. The two most widely used averages are the 50day and 200day moving averages. Moving averages are not just used by specially trained market technicians. Even financial analysts with Harvard MBA’s will refer to the 50day and the 200day moving averages as frequently as they do to P/E ratios and earnings growth rates.
In the previous articles, I talked about how moving averages help us get a good “read” of a stock’s price chart. We’ve seen how to use moving averages to determine the dominant trend of a stock or index, as well as the ideal points at which to enter or exit that trend. Today I want to bring my discussion of moving averages to a close by talking about one more way to use moving averages. This is, in fact, the most profitable technical trading strategy I use. In this strategy we are stressing the idea that certain moving averages – particularly the “big two”, the 50day and 200day averages – act as “magnets” for the price of a stock or index whenever it moves too far away from the averages.
The phenomenon I am referencing here has a fancy label. It’s called “mean reversion”, and it recurs so often in financial markets that studying it and learning how to profit from it have become a kind of cottage industry. Some of the world’s greatest financial minds, including Ivy League professors and Federal Reserve Bank economists, have published peer-reviewed papers on the subject.
The idea behind mean reversion, in a nutshell, is this: a moving average of share price represents the accumulation of wisdom on the fair market value of a particular company’s shares (and hence, of the company itself), while the day to day fluctuation in share price is more a reflection of the ever changing whims of market sentiment. Thus whenever that sentiment drives share price too far from its average, the efficiencies of market forces being what they are, share price is bound to revert back to its mean in short order.
Determining just when price has been stretched too far from its average, and just how far back to the mean it will travel when it does revert, is more art than science. But there is one tool that can help us greatly with this determination. Using past price performance over X number of time intervals, this tool measures standard deviation from a moving average and draws bands on the chart, both above the below the average, that show the upper and lower limits of that deviation. It is expected that price will stay contained within these bands going forward. This would be “normal” price action. Any move above or below the bands, therefore, signals an “abnormal” move beyond the standard deviation, hence an overextension that is likely to revert to the mean.
The tool I’m talking about here, of course, is Bollinger Bands, developed by market technician, John Bollinger, back in the 1980’s. I’ve used these bands for years and can say that, like most technical indicators, they work well when they work! But when the Bollinger Bands are not working well, your trading based on them can go horribly wrong. Still, when we couple the Bands with stop-losses to minimize the damage, they are the best tool we have for determining regions of price extremes where a stock or index is likely to get over-extended and flip back to the mean.
Let me show you some examples. In the chart below you’ll see shares of EBAY, Inc. (Nasdaq: EBAY) with the 200day moving average overlaid, along with the upper and lower Bollinger Bands set at 1.5 standard deviations away from the average. You can see how over the past 9 months, whenever price travelled outside either band, it was only a matter of time before it bounced back the other direction.
I used a standard deviation of only 1.5 on the 200day moving average in the above chart because it takes a significant move to get even that far away from such a long-term moving average of price. When we shorten our time frame down to the 50day average, however, we’ll need to increase our deviation from 1.5 to 2.0 to reduce the noise of false signals.
Here is a chart of Amazon, Inc. (Nasdaq: AMZN) during a rather inefficient, tumultuous time in the stock’s recent history. I have here overlaid the 50day moving average with bands set at 2.0 standard deviations away from the average. You can see a greater number of signals compared to the chart above, and also that some signals would have been more profitable than others.
As you work with Bollinger Bands, you will want to refine your trading system. You cannot simply buy every dip below the lower Band and sell short every rally above the upper Band. As you experiment, I suggest integrating some of the following suggestions into your trading system:
Only take buy signals at areas of price support and sell signals at areas of price resistance Don’t trade minor moves beyond the Bands but only those that exceed the Bands by a certain percentage (you can use the %B Bollinger Band Indicator for this determination) Try entering only when price closes outside the Bands on one day, then closes inside the Bands on the next day Only take trades when Bands are wide and avoid trades when Bands are constricted.
Mean reversion theory is a well attested phenomenon that, when learned well and traded appropriately, can be a very profitable approach to the markets. If you are looking for more resources on this trading system, you might want to try the Mean-Reversion Trading Manual I offer on my website, DrStox. You can also look at my book, Market Neutral Trading, where I fully explain how I use this trading system. But certainly the original source is always a good place to start too: Bollinger on Bollinger Bands, the “bible” of the Bands!
Recent free content from Dr. Thomas Carr.
$MDCA - Buy The Breakout? Yes! — 7/24/17 Buy MOMO Now Before the Bounce — 7/05/17 Can CONN's Carry On Higher? — 6/23/17 Can the Rally in $CVNA Continue? — 6/20/17 Spicy $SAUC a Great Buy Here — 5/23/17.
Send Message to.
We've added you to the list!
You'll start receiving free content from Dr. Stoxx Options Letter right away!

Mean reversion strategy pdf

Last week I discussed in great detail one of my favorite option strategies, the bear call spread. I also talked about my approach to bear calls spreads in great detail during my latest webinar. Click here to watch.
So, with that being said, I’m not going to go over the basics of the strategy. Instead, I want to dig further into the strategy by discussing a potential trading opportunity.
As you can see in the SPDR Gold Shares (NYSEArca: GLD) chart below, the GLD fund has pushed significantly higher over the past few weeks. As a result, the RSI (2) and RSI (5) are in a “very overbought” state.
When this type of short-term move occurs, mean reversion – the tendency for a stock to return to its average price – usually kicks in. In this case, GLD has moved several standard deviations away from the mean.
Think about a “very overbought” move in terms of the standard bell curve. When something is “very overbought” it has moved to the outer fringes of the curve.
There was only a 4.79% chance that GLD would push to where it’s trading now. The options market, as seen in the options chain for GLD below, stated that there was a 95.21% probability of success that GLD would close on Oct. 15 below $113.50.
So, we know the move is somewhat of an anomaly.
When a move like this occurs and we see RSI push to “very overbought” levels, I immediately want to fade the directional move. When I fade a move – in this case a bullish move – I am hoping for a short-term reprieve in the underlying price. The price could move lower, trade sideways or simply plod slowly higher. I’m just hoping for the laws of mean reversion to kick in.
But I increase my pot odds by wrapping a high-probability strategy around the trade. Rather than take a directional bias and simply buy puts, I want to sell calls, more specifically bear call spreads.
In this case, since GLD is currently trading for $113.81, I want to sell a call at a higher strike. But which strike? I always start my search with the strike that has an 80% probability of success. What that means is that at expiration in 37 days, there is an 80% chance that GLD will close below that strike.
As you can see from the option chains above, the 119 strike meets my requirements.
We can sell the 119 call strike and buy the 121 strike for a net credit of $0.27. To find the credit, take the bid price of the 119 strike we sold ($0.88) and subtract the ask price from the 121 strike we bought ($0.61).
Our return on the trade: В 15.6%.
Basically, as long as GLD stays below our short strike at expiration we will reap the entire premium of 15.6%. There is a 78.48% probability of success that the price of GLD will stay below our short call strike of $119 at expiration in 37 days.
But don’t forget, we are wrapping a high-probability short-term trade on an ETF that has already pushed into a “very overbought” reading. This increases our pot odds on the trade that much further and it’s why I’m not placing trades every other day. It’s a methodical, patient approach.

R&D Blog.
I. Trading Strategy.
Developer: Richard Wyckoff. Concept: Trading strategy based on false breakouts. Research Goal: Sensitivity of Wyckoff patterns. Specification: Table 1. Results: Figure 1, Figure 3. Trade Setup: Long Trades: Price moves below a trading range and reverses back into the range (“Bear Trap”). Short Trades: Price moves above a trading range and reverses back into the range (“Bull Trap”; Figure 2). Portfolio: 42 futures markets from four major market sectors (commodities, currencies, interest rates, and equity indexes). Data: 33 years since 1980. Testing Platform: MATLAB®.
II. Sensitivity Test.
All 3-D charts are followed by 2-D contour charts for Profit Factor, Sharpe Ratio, Ulcer Performance Index, CAGR, Maximum Drawdown, Percent Profitable Trades, and Avg. Win / Avg. Loss Ratio. The final picture shows sensitivity of Equity Curve.
Tested Variables: Entry_Index & Cancel_Index (Definitions: Table 1):
Figure 1 | Portfolio Performance (Inputs: Table 1; Commission & Slippage: $0).
1. Correction ≥ Pattern_Size; Pattern_Size = 10 bars (Default value).
2. Price(D) ≤ Cancel_Level, where:
Cancel_Level = Price(B) + |Price(B) − Price(C)| * Cancel_Index.
Long Trades: Price moves below a trading range and reverses back into the range (“Bear Trap”). There are two conditions:
1. Correction ≥ Pattern_Size; Pattern_Size = 10 bars (Default value).
2. Price(D) ≥ Cancel_Level, where:
Cancel_Level = Price(B) − |Price(B) − Price(C)| * Cancel_Index.
Cancel_Index = [0.2, 3.0], Step = 0.1;
Entry_Level = Price(B) + |Price(B) − Price(C)| * Entry_Index.
Short Trades: A sell stop is placed one tick below the Entry_Level, where:
Entry_Level = Price(B) − |Price(B) − Price(C)| * Entry_Index; (Figure 2).
Reward Exit: Long Trades: A sell limit is placed at: Entry_Level + |Price(B) − Price(C)| * Reward_Index. Short Trades: A buy limit is placed at: Entry_Level − |Price(B) − Price(C)| * Reward_Index.
Stop Loss Exit: ATR(ATR_Length) is the Average True Range over a period of ATR_Length. ATR_Stop is a multiple of ATR(ATR_Length). Long Trades: A sell stop is placed at [Entry − ATR(ATR_Length) * ATR_Stop]. Short Trades: A buy stop is placed at [Entry + ATR(ATR_Length) * ATR_Stop].
Entry_Index = [0.1, 1.5], Step = 0.05.
ATR_Stop = 6 (ATR.
Average True Range)
Table 1 | Specification: Trading Strategy.
III. Sensitivity Test with Commission & Slippage.
Tested Variables: Entry_Index & Cancel_Index (Definitions: Table 1):
Figure 3 | Portfolio Performance (Inputs: Table 1; Commission & Slippage: $100 Round Turn).
IV. Rating: Richard Wyckoff – Mean Reversion | Trading Strategy.
CFTC RULE 4.41: HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT BEEN EXECUTED, THE RESULTS MAY HAVE UNDER-OR-OVER COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFIT OR LOSSES SIMILAR TO THOSE SHOWN.
RISK DISCLOSURE: U. S. GOVERNMENT REQUIRED DISCLAIMER | CFTC RULE 4.41.
We share what we learn.
Sign up to receive research news and exclusive offers.

Mean reversion strategy pdf

This is a simple trading strategy that provides some core mean-reverting properties. It involves the following:
If the current price is greater than the upper bollinger band, sell the stock.
The bollinger bands are calculated using an average +- multiplier*standard deviation. The average in this case, is calculated by a linear regression curve because a simple moving average is often a lagging indicator and becomes a big problem with long look-back periods.
Playing around with the look-back period can provide some interesting results, try it out!
Thoughts and suggestions are always welcome.
More on the strategy can be found here.
Updated code to fix high and low bollinger bands.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Can you rewrite this so we can backtest it against individual stocks rather than the whole market that would be appreciated!
Done! I also fixed where the lower bollinger band was missing. I've set it up just using the S&P500 but you can modify the sid to you're liking.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Seong, this is an fascinating algo.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
It looks like in your handle_data function you have "context. days_traded += 1". Doesn't this function run every minuet in a full backtest? Wouldn't that cause the check to happen every 20min as opposed to 20 days?
Thanks for mentioning that, I hadn't thought about how it would work in minutely data as I only tested it in daily data, but here's a way to test it in minutely data as well.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
I'm unaccustomed to reading Python code, so I may be missing something, but where is the "exit position" command in your code? I see you buying 5000 shares when you're below the lower threshold and selling when you're above the upper, but I don't see you exiting anywhere in the middle. I ask because, in the header, you say that positions are exited when the price crosses the moving average.
Also, are you using leverage here?
Another question: you say that you're using the intercept of the linear regression curve, but isn't the second returned value of linregress (indexed by the number 1) correspond to the slope of the regression line? Again, new to Python, so I could be very wrong.
Unlike the futures market, the long side of stock markets work quite differently than the short side, at least that is what I have seen. It is probably because we humans react differently to greed and to fear. The short sides are quick steep drops lasting for short periods, while the long side is more gradual climbs and lasts longer. Based on that, the mean reversions need different parameters to work on both short and long sides. I love to see the exchange of ideas and generosity of the able coders here.
Thank you everyone for sharing.
To my knowledge, linregress 1 returns the intercept of the linregress line while [0] would return the slope , more here.
And you're right about the exit position, there is none for now, will get on that soon. And yes, there is a bit of leverage used here although as to how much would depend on the order amount.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Ah yes, you're right about linregress. From a statistical point of view, that is a very strange choice on their part.
Are you able to run the strategy without any leverage, so we could get an idea of what the returns would be in that situation. I ask because I've played with similar strategies that gave nowhere near the same performance as yours, but they've been unleveraged, so I want to make sure I'm making a fair comparison.
Still working on the leverage, but I've incorporated exit positions into the algorithm and the returns are very different. If you'd like to find out more about leverage there's a Quantopian thread here as well. The current exit position is whenever the price crosses the mean, and I think there'd be a better exit position than that especially with the 20 day lookback period on that. If you have any suggestions on that, please feel free to post.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
The latest backtest I've uploaded doesn't use leverage so you could use that as a good way to compare your tests to mine.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Here's a way to adapt it to minutely data (which works!), by using a check to add prices only once per day (at the close) you can effectively store close prices into an array in order to perform the linear regression method.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
this works better.
Marco, sorry newbie here. but the algorithm you posted is very different than Seong Lee's linear regression method/code. It seems closer to quantopian/posts/simple-algo-that-tries-to-earn-money-on-speculators. Did you post in the wrong thread? Can you outline any new changes you made. it makes it easier to see the new code changes.
When I cloned and run your algorithm, I got the following warning. so, is there any reason why you used batch_transform here other than history ? Would you elaborate more on how batch_transform work here ?
" Warning batch_transform is deprecated, please use history instead."
I created this algorithm before 'history()' was released. 'batch_transform' is very outdated and we don't recommend you to use it anymore, instead please use 'history()' which allows you to query for X amount of historical data starting from the backtester's current trading date.
So if you wanted the past 20 days of trading data you would do:
The last version that I have here uses history to query for past data, feel free to use this one instead.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Well I feel that if rather than buying when close price crosses lower Bollinger for the first time, you should buy it once close price resurfaces and equals the lower Bollinger (and similarly for shorting also).
Have you ever heard of overfitting? The algorithm doesn't perform well on untrained/unseen data. Try e. g. to run the algorithm from 2013-2016. Walk-forward testing among other things are needed! ;-)
The strategy was published on October 2nd, 2013!
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Can anyone help me change this algo to something smaller? Every time I try to adjust it to say 3,000 it gives me a return of 29,000%. What's up with that? BTW I'm a total noob.
The problem here is probably related to your order, being way too large. What's happening is that you are buying and selling lots of 3000 shares which makes your strategy unreasonable. For a good ressource on order types, try:
Sorry, something went wrong. Try again or contact us by sending feedback.
You've successfully submitted a support ticket.
Our support team will be in touch soon.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.