Tag: trading

Traders – society’s useless souls

April 5, 2014

In response to: https://www.youtube.com/watch?v=LTAJwtWoQg4

Tom makes valid arguments for allowing the general market system, in the US, to evolve naturally, which it has, including the adoption of liquidity provider skimming through HFT because – in general, everybody benefits from better technology all around. And monies earned through order routing and fulfillment, however it is done, at the greater and greater speeds and smaller and smaller spreads help to give the end users, the traders, better and cheaper information, technology, and ultimately, execution.

What about IEX then? What about the the adoption of the continuous Dutch auction concept of a pulsed market? And, ultimately, and this is the big big question, how does society benefit from this escalation of trading technology?

Society benefits from investors and investment. Investing can undoubtedly be done at a much slower pace, once a day let’s say, and still perform the function it has in society. Investors are in it for the long haul. Investors care about future returns, growth and general economic success, not just for themselves but for society as a whole.

Trading is not investing.

Trading, it can be claimed, is a required superstructure to allow investing. True. But the original purpose of an exchange was to allow investors to buy and sell, at an investors pace, investment vehicles. Trading was simply the term used for buying and selling. Trading has become an activity that now supersedes investing. Earning a living through exploitation of the friction of the investment machine is what traders do now. How does this benefit society? Does this benefit society? I would posit that trading, in fact, detracts from society. That the energies, talent and monies driven into the trading profession extract valuable resources from society. Traders create nothing, produce nothing and generally earn a living from the grist of the machine.

Here’s a simple question to ask as to the usefulness of traders in society: what would society be like without them?

If we lost all doctors – that would be bad.
Losing all nurses, dentists, police, teachers – bad bad bad bad.
All construction, aerospace, integrated circuit engineers – bad bad bad.
All farmers, all lawyers, all journalists, all accountants – bad bad bad bad.
You get my drift.

Now what if we lost all traders? Meh, good riddance.

If every trader on the planet suddenly stopped trading and and started doing some other occupation what would society lose? Nothing. Investors could still find each other through services that might popup like Ebay or CraigsList for investments. Buyer meets Seller can be done much more cheaply and without the millions of traders who inject themselves into the process. Remember investors could exchange their value for value instruments once a day or even less frequently and still be fine with the prices they get. They’re in it for the long haul after all. Investors don’t need or want to exchange their investment a million times per second. Only traders think they need to do this – and why – pure grist from the machine.

The fact is society could do without only very few occupations in this day and age – but one of them is most certainly – traders.

Strategy characteristics to enable trading

January 19, 2014

My personal list of strategy characteristics required to start trading, in descending order of importance:

1) An explainable, cogent reason why the strategy’s signals are profitable (in bulk).
2) Consistent performance of a random subset of a specific instrument type over time (out of sample) and across random selections of instruments within the asset type.
3) Drawdowns as a percent of account less than 10%.
4) Limited to no human intervention required.
5) Trade sizes that can scale.
6) A high enough frequency of trades such that skepticism in the strategy’s operation is not called into question.
7) A uniqueness or eccentricity in the strategy’s technique such that the likelihood of discovery or loss of efficacy is limited.

So far I have built strats that satisfy 1, 4,5,6 and 7. It’s 2 and 3 that are the problem spots.

Some of my rational:
1) If you don’t understand it – you won’t trust it. Blackbox style NN algos that are completely opaque as to logic – how are you going to trust them when they start a losing streak?
2) Sector based or venue based selection is good. Grains don’t trade like energy futures. Utilities don’t trade like tech stocks. But you should be able to randomly select from within your sub sector or category and perform adequately with any sub selection. And of course, a strat should be able to test in and out of sample, walk forward style, over vast amounts of data.
3) After you lose 10% of your bank, you’re gonna question the strategy’s potency.
4) I don’t want to sit in front of this computer screen(s) and trade. Not my thing.
5) FX you can trade for pennies, or millions of dollars. mini-futures, a few highly liquid securities, an option or 10, all scale very well. Bond trading? Low liquid stocks? Full contract futures? Not so much. But you want a strat that you can pile into after you start winning. So, the instruments needs to scale.
6) A strategy that trades 5-50 instruments, at least once a day (in total), means it’s working, and you can be fairly comfortable in its operation. Once a week? Sure for investing – not trading. More than 100 time a day? I doubt anyone could walk away from a beast like that and golf the mornings away.
7) Belief in your strat’s ability to profit is paramount. A generic block of logic you pulled out of S&C magazine is not a candidate for trust. Something in your strat, its logic, instrument, time of day, scaling, stops, basket technique, pairs algo must be unique ( as far as you know) so that you can trust it when it starts to lose.

Technical strategy development

January 5, 2014

Posted on Quantopian.com

Profit (and loss) for a single trade are closely tied to the type of strategy being traded. The two primary strategy types you’ll find on most technically based platforms are momentum and ranging. The primary types of momentum strategies are breakout and trend following where you’re betting on a long run. There are many types of range strategies, but statistical arbitrage is the biggest subgroup. With a stat-arb strat you’re trying to carve out an anomalous price move from a flurry of normal moves, whether you’re trading a single security on a bevy of technical indicators, or pairs arbitrage, or basket arbitrage you’re betting on reversion to mean. Another way to think of the two types of strategies is in momentum type, you’re buying strength and selling weakness. In stat-arb, you’re (generally) selling strength and buying weakness.

With these two types of strategies in mind you can now pick the one that you are trading and then apply the appropriate exit. When it comes to momentum strategies think “Turtle traders.” You’re willing to take lots of small losses on false momentum signals while waiting for the big kahuna to show up. So well defined stop losses are imperative. For such trading scaling in is often advised, when you’re winning, you add size. The profit target on a momentum trade is the opposite of the entry; on momentum exit signals (reversals) you’ll be scaling out of your position, knowing that you might be getting faked out on some of the signals. A momentum profit target is often an order of magnitude larger than your stop loss. You’ve heard the saying “let your winners run” no doubt. This only works for momentum strategies. Cutting short a winning momentum trade, as if it were a stat-arb trade, is a common mistake.

For the various stat-arb style strategies profits are much smaller and may even be equal to your stop loss, as long as your technique leaves you with a high win to loss ratio. Volatility is an important aspect to most arbitrage strategies. If you’re looking to capture reversion to mean you need to know how far away the mean is, usually in ATR units. On the entry to a common arb strat you’ll calculate the probability of price moving against you x ATRs vs the probability of price moving in your favor y ATRs. Knowing these levels prior to entry, or just after entry, is important to calculate your risk. Traders often talk about reward to risk ratio. A 2 to 1 ratio, generally for stat-arb or swing trading, is a standard. Lose 1 ATR and win 2 ATRs is one way to look at this.

Swing style strategies are what I think to be a blend of momentum and arbitrage. There may be a bit of mis-pricing that you’re trying to capture in addition to over all market momentum. In such trades, what are called measured moves are often useful. Ways to measure a potential profitable move could be taking the height of prior similar moves, either up or down, Fibonacci levels, target support and resistance levels, or just plain ‘ol technical exhaustion.

The key to establishing profit targets is knowing what strategy type you’re trading, your expected win/loss ratio, and the amount of risk you’re willing to take on any one trade.