Monthly Archives: January 2014

Strategy characteristics to enable trading

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

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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.