15
votes
Usage of Random forests in Quantitative analysis of stocks
Recently I attended a presentation by the first author of the following paper who gave us quite a creative and illuminating (kind of meta-)use of random forests in Quant Finance:
All that Glitters Is ...
- 27.3k
15
votes
Accepted
Why do trading strategies lose effectiveness over time?
There are two key concerns (which in practice, may be difficult to distinguish):
Previous research overestimated an effect.
The effect shrinks over time.
1. Problems with reproducibility and ...
- 6,924
15
votes
Doing opposite of what the model says
If you do this, you would destroy the value of the statistical tests that you performed on the backtest. You had a hypothesis that the strategy would make money, but the hypothesis was rejected. You ...
- 9,332
14
votes
Accepted
Why are there so few published research papers that apply Deep Learning to Algorithmic Trading?
I would say that most ML methods risk overfitting and it depends very much on the asset class. The only area where more sophisticated ML methods such as deep learning appear to make a major difference ...
- 1,068
13
votes
Quantitative strategies in the Fixed Income space
Here are some general directions:
Alternative Risk Premia
The ARP, or "smart beta," space has gained a lot of tractions over the past few years. These are rule-based strategies that provide ...
- 11.4k
13
votes
Accepted
How long do algorithmic trading strategies typically remain profitable?
Is there a typical "half-life" of a strategy?
This is a really subjective question, and I don't think any singular answer will generalize well. That being said, I will give some examples ...
- 4,738
12
votes
Accepted
statistical arbitrage vs factor trading
1) Why would you trade the error on the residual instead of creating a long/short factor model and trade expected returns?
I would posit that the biggest reason people do this is for orthogonality of ...
- 446
12
votes
Accepted
What are the "sniffing" or "stalking" algorithms?
Sniffing (or stalking) algo indeed detects other algorithms. How does that work in practice?
Imagine the order book for a particular equity is: Bid 1 = 99 (size 10,000), Bid 2 = 98 (size 25,000), Bid ...
- 5,998
11
votes
How to calculate Sharpe Ratio from $ returns?
Let's say your cumulative return series is $\{R_i \mid i=0,1,...,N-1\}$ of length $N$ days.
There's 3 conventional ways to do this at this stage. You may convert the cumulative dollar return curve ...
- 5,231
10
votes
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Online sources for quantitative finance research
Below are some of the sources one can use to search for and view or download research articles and other publications on quantitative finance (QF). Many include non-peer-reviewed articles in their ...
- 1,026
9
votes
What is the best alternative of Quantlib library
The Strata project is the new pure Java market risk quant library from OpenGamma. For more information, see the documentation and GitHub. It is Apache v2 licensed.
Strata takes the experience of the ...
- 292
9
votes
Accepted
What is an acceptable Sharpe Ratio for a prop desk?
A Sharpe ratio of at least 1 in backtesting is a promising start, but that is just one of many statistics of interest. The Sharpe ratio measures return per unit volatility, i.e., return per unit risk....
- 415
9
votes
Accepted
What is the appropriate benchmark for a Long/Short VIX futures strategy?
If your strategy truly has no directional bias, then the benchmark should be cash (ie whatever you would earn using the capital in your trading account and taking no risk).
- 5,891
8
votes
Why are Quantquote historical trades different vom ActiveTick historical trades
This is an old post, but I thought I would offer the following facts:
1) QQ claims to be sited in the Empire State Building, Suite 2100. (https://quantquote.com/contact.php) That is false. They do ...
8
votes
Is trading mean reversion of small principal components of prices profitable?
Within the fixed income space, there's a lot of literature on PCA trading.
The first 2-3 principal component factors (PCs) can typically explain 90-99% of the total variances in yield curve movement....
- 11.4k
8
votes
Accepted
Why do anomalies disappear after they get detected?
The best explanation I have seen so far is the so-called Adaptive Market Hypothesis by Andrew Lo:
The adaptive market hypothesis, as proposed by Andrew Lo, is an
attempt to reconcile economic ...
- 27.3k
8
votes
Accepted
What is the reason for using log prices in Pairs Trading (Cointegration)?
I'm assuming that the paper you're referring to uses the Engle-Granger test for cointegration. The standard test procedure checks for unit roots in the residuals of a linear regression. It is a "...
- 2,368
7
votes
Is it possible to make profit by reversing client trades for a market maker?
If you are market making equities or futures you tend to make your profits over the short term by flipping your inventory. So if I'm showing 3.00 bid at 3.01 ask on a stock I'm going to tend to flip ...
- 969
7
votes
Accepted
Algorithmic Trading: Python vs SQL
Python has lots of excellent libraries to compute Technical indicators for you, ta and ta-lib are great. These libraries have ...
- 647
7
votes
Known mispricing opportunities only available for small traders
The claim that there are small opportunities that are overlooked by large institutions is increasingly untrue. Some large firms specialize specifically in aggregating a large number of low capacity ...
- 2,368
7
votes
Accepted
Formerly profitable algorithmic trading strategies?
Take a look at compilations such as 151 Trading Strategies.
I wouldn't expect this information to be widely disclosed. After all, a non-profitable strategy is a supermartingale which means there is an ...
- 1,162
7
votes
If 90% of retail traders lose money, doesn’t that mean price movements are not random?
Markets might be nonrandom but retail traders losing money on average is insufficient evidence of this. Most folks lose money playing roulette despite the outcome being random. As noob2 suggests in ...
- 8,438
6
votes
Accepted
Momentum - skipping the most recent month
The idea of skipping a month was already in Jegadeesh and Titman 1993. The key academic paper in this area.
Jegadeesh himself (without Titman) discovered a 1-month return REVERSAL effect in ...
- 10.9k
6
votes
Accepted
What are some quantitative trading strategies used by high-frequency trading companies to make a killing on a market crash day on 24Aug2015?
On aggregate, large shops like Virtu are involved in market making strategies. There's various classes of market making strategies, and it is unnecessary to distinguish further here for the purpose of ...
- 5,231
6
votes
Why are Quantquote historical trades different vom ActiveTick historical trades
I have used QuantQuote minute data previously and found it to be by far some of the best data I have used. It's interesting to observe that DonaldRC has just a single post and is pushing ActiveTick ...
- 161
6
votes
What is the best alternative of Quantlib library
I did not tested it by now, but Google released a library similar to quantlib written in TensorFlow (tf-quant-finance). It may be worthwhile to test it (and to post here your views on it), because ...
- 11.5k
6
votes
Accepted
Why is there a stong intraday-correlation between spot and vol?
This effect is coming from the supply and demand in the options markets. Many portfolio managers want (or need) to buy out of the money put options, and many are willing to sell out of the money call ...
- 16.5k
6
votes
Accepted
R Backtesters: Quantstrat vs SIT
While I've never used SIT, I have used quantstrat quite a bit and can attest to its strength. It has a solid developer community backing it (7 contributors on Github), is part of the TradeAnalytics ...
- 636
6
votes
What is smart beta, alternative index, factor investing?
In recent years there has been much attention given to defining indexes other than market-cap based indices. While market-cap based indices approximate the theoretical Market Portfolio enshrined in ...
- 10.9k
6
votes
Does QuantConnect use both bid and ask data for backtesting?
QuantConnect uses L1 data (bid and ask quotes) for its US Equities Backtesting.
QuantConnect has a full break down of the data library, including free data for download in LEAN format at the data ...
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