# Tag Info

27

Aside from Zipline, there are a number of algorithmic trading libraries in various stages of development for Python. From the commercial side, RapidQuant looks very interesting though I haven't tried it yet. It's from some of same developers that brought us the excellent Pandas data analysis library. I think Wes McKinney (Pandas's main author) is involved....

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Edit (2016-06-21): Now with live data/trading integration with Interactive Brokers. It has taken a while but it has finally arrived. Edit (2017-09-20): live data/trading includes Visual Chart and Oanda (legacy accounts), order types, timers and market calendars, update with Python 3.6 and the community and other links updated A (now) very mature (imho) ...

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Windham Capital Management is using hidden markov models for their Risk Regime Strategies. Mark Kritzman, who is also CEO, has published an article about the general outline of the strategy (with source code so you can replicate the results!): Regime Shifts: Implications for Dynamic Strategies (corrected August 2012) by M. Kritzman, S. Page, D. Turkington]...

12

After having done a lot of research on the topic I found the following excellent research piece on ETF.com: Wealthfront modifies historic asset-class returns with current market implied expected returns (Black-Litterman) as well as with the in-house views of Chief Investment Officer Burton Malkiel’s team. In addition, Wealthfront sets minimum and ...

12

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 3 = 97 (size 30,000), Offer 1 = 101 (size 10,000), Offer 2 = 102 (size 25,000), Offer 3 = 103 (size 30,000). So in the example above, the bids and offers are ...

11

You need to differentiate between OTC and listed options in order to appreciate the fact market makers are still active and relevant in either segment: Listed Options: Actually most listed options market making is governed by market making algorithms, however, most such algorithms are implemented with manual overlays. Something very similar goes on in the ...

11

Repeating groups are a way for FIX to represent arrays. A "number of" field prepends the repeating group to alert the recipient how many elements to expect. For example, Arca uses TradingSessionID (tag 336) to identify pre-open (P1), primary (P2), and post-close (P3) market hours. This group is prepended by NoTradingSessions (tag 386). So, I would use the ...

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I found this solid overview of different trading algorithms by Deutsche Bank Research: Trade execution algorithms Designed to minimise the price impact of executing trades of large volumes by ‘shredding’ orders into smaller parcels and slowly releasing these into the market. Strategy implementation algorithms Designed to read real-time market data and ...

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A public order book gives traders information not only on the current price of a security, but also the volume and structure of the entire supply and demand schedule. Such information can be used for arbitrage and market manipulation strategies in various ways: Spoofing: Inserting a large limit order as an apparent buy or sell signal which is canceled any ...

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Here's my favorite example of an intraday strategy on S&P500 futures that at least used to work: Intraday Share Price Volatility and Leveraged ETF Rebalancing I pull it out whenever people start talking about market efficiency. The strategy is very simple: if S&P500 futures are up or down more than 2% on the day with two hours left until close, ...

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In January 2020, Matteo Aquilina, Eric Budish, and Peter O’Neill from Britain's Financial Conduct Authority published this study, illustrating how "low latency" market participants can make money off of others. I suggest you read it, because it's very clearly written for the general public, and explains how markets work. I will first oversimplify ...

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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 from personal experience. I have made hundreds of trading models in my career. I have only deployed 9 into live trading in the last ~25 years. Of those 9, 2 of ...

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Firstly, you'll probably be directed to consider Zipline. It's worth a look but I don't think that it's a good starting point, since: Quantopian's developers don't have a financial background and it shows through in the Zipline source code. Zipline is dreadfully slow if you compare it to any commercial platform with backtesting functionality in a compiled ...

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I am not sure Dark Pools (DP) have been created to avoid "market manipulation". They have been created by firms because they found an advantage to create them (see Market Microstructure in Practice, L and Laruelle Eds.). The main reasons have been: spare market fees, for DP created by brokers (like UBS MTF); spare market impact, for block pools (like ITG/...

9

Indeed, algorithmic trading is a very hidden subject. All I can help you with are some industry-specific terms which might speed up your search for relevant papers and information: Risk of ruin tables (Peak-to-valley) drawdown (maximum drawdown, duration of drawdown etc.) Number of consecutive losses Confidence intervals Empirical distributions (for risk ...

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I think you might find this answer in The future language of quant programming? useful. People get this problem wrong because they always end up discussing the theoretical advantages of these languages rather than the practical uses of these languages. Theoretically speaking: Haskell is elegant and has many of the theoretical advantages (language ...

9

First we have to clarify what we mean by profits: I think your question can only address the fact that some human traders beat the market (because you also make profit by just buying the market, e.g. through an ETF). I think there are two, perhaps even three main sources: Randomness, luck (as @PerAlexandersson) correctly pointed out - financial markets are ...

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Why does algorithmic trading account for a significantly higher percentage of trades in the USA than in Europe or Asia? One of the major reasons for this is the significant fragmentation in the U.S. markets, and in particular the U.S equity markets where I believe Aite Group's data (in your picture) comes from. This doesn't necessarily provide an edge to ...

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In addition to getting the right transition model for the Kalman filter, the main obstacle to optimizing filter performance is to implement an optimal initialization. I use an iterative approach to initialize or "tune" the Kalman filter, known as adaptive tuning. I do this because I've found alternative methods of initializing the Kalman filter (...

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You would definitely have some advantage. High Frequency Trading is all about speed and the fastest traders wins. Oftentimes, winner takes all. The blog Sniper in Mahwah & friends digs into the state of the art of inter-exchange communication. The current state of art for reliable broadband connections are microwave dishes between major trading hubs such ...

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At the first glance, what you are asking for is a model admitting arbitrage, so there is a zero chance of losing money and positive chance of yielding profits. Well, many equilibrium models start with assuming arbitrage is not possible (otherwise it would be trivial wouldn't it). But, in my opinion, what you actually seek is the Efficient Markets Hypothesis....

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Unfortunately, the answer is: it depends. People care about different metrics and visualizations depending on the type of strategy that they are running. It is a very bad idea to spend time creating visualizations without knowing what you are using those visualizations for. A common feature is a 'table-oriented' layout of data about your orders and ...

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Such a complex question... Geometric Brownian Motion (GBM) will not typically work to aid one finding strategies based on technicals, as the pursuit of the technical trader is to find market deviations from a random walk. However, some strategies, for example a "take profit/stop loss" strategy can work, (or at a minimum one can change the risk/reward ...

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I was searching for answers to the same question and came across your question. After some thought and research, here is the plan I have developed. I will be working in Python. Calculate relative maxima and minima with SciPy. Calculate RSI at those points using lib-ta. For each pair of lows and highs, compare the change in price with the difference in RSI. ...

7

As someone who has contributed to literature, I am purposefully vague with the use of mid price. Not that I don't define it but that it is difficult to state which definition is the best in which context. Here are an example of a few definitions of mid price: Last Trade: The physical price at which the most recent trade physically took place. This is ...

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possible update: http://pmorissette.github.io/bt/ based on http://pmorissette.github.io/ffn/ both were easily installed and somewhat usable for a novice. would love some examples other that github documentatiion

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On the request, here are my two cents. Suppose that the price follows the dynamics $$\begin{cases} \mathbf z_{k+1} &= F(\mathbf z_k,\mathbf i_k,\mathbf w_k), \\ \mathbf i_{k+1} &= G(\mathbf i_k, \mathbf w_k) \end{cases}$$ where $\mathbf z_k$ is a price of a traded assets at the time $k$, $\mathbf i_k$ is the value of parameters of the ...

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You are right, these work use deterministic control. Framework using stochastic control exist: Bouchard, B., Dang, N.-M., Lehalle, C.-A., 2011. Optimal control of trading algorithms: a general impulse control approach. SIAM J. Financial Mathematics 2 (1), 404-438. URL http://epubs.siam.org/doi/abs/10.1137/090777293?af=R Kharroubi, I., Pham, H., Jun. Optimal ...

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To be honest you're not likely to get a very satisfying answer to your question. Not because its a bad question, but because "regular people" can't just go hooking their home grown trading systems up to a live market. I'd like to start automating my trading strategies. First off you'll need a system that can interface with your broker. If you're not a ...

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'Machine learning' describes a very broad spectrum of algorithms. Just briefly here are a few conceptual areas; Neural networks Reinforcement learning Genetic algorithms and genetic programming Particle swarm optimisation (PSO) Regression models Optimisation routines Markov models Wavelet transforms and Fourier Transform and Spectral Analysis. Clustering ...

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