As a tribute to the many ideas and insights I gathered from this forum, I’d like to share an approach that is serving me well. Ahead of explaining the approach, let me be clear: even though I have developed/coded all necessary tools by myself and they are readily available, I do not intend to share (nor support) them, so don’t bother asking me for them. I’d be going into all their relevant inner workings, so if there is anybody interested in pursuing the approach, it should be easy to reproduce them.
I am using a combination of regression calculations on currency pairs and related indices to get an overall directional bias, where the pair is heading (=trend). I am well aware of the non-stationary character of time series and the questionable value of statistics derived from them. Nevertheless, it is always a perception we are trading, and as such those regression channels help me build mine.
I built an indicator for each of the 8 currency indices from bar zero to 1440 on a 60 min chart (=3 months). The formula for the usd index is well-known: usd= ((USDCAD*USDCHF*USDJPY)/ (AUDUSD*GBPUSD*NZDUSD*EURUSD))^1/8. For the aud index it is aud=usd*AUDUSD, for cad=usd/USDCAD, etc.
It further derives the temporary “optimal” (in terms of variance) sample size (of bars) by iteration. This is done by running linear regressions and tracking the normalized coefficient of variation NCV (i.e. (StdError/Mean) / (Sample size – 1)^1/2 over all the 1440 samples per index starting with a minimum of 72 (=3 days). The algo will stick with the sample size with the smallest NCV. The consequence is that each currency index ends up with an individually optimized sample size rather than applying the same “look-back period” across all of them.
The third and last step is then to run the same iterations of regressions across the traded currency pairs and compare the resulting biases with the ones of the relevant indices – e.g. CHFJPY bias with CHF and JPY biases. If they all align, e.g. JPY up (=strong), CHF down (=weak) and CHFJPY down, I would only looking to place sell orders.
For entries (and exits) I pick a straight-forward pivot indicator showing Pivot, S1-2 and R1-2 levels. You can place pending orders on those levels above (in case of a short bias) or below (in case of a long bias) current price, but they should be either close to the line-of-best-fit (LBF) or better in pull-back zones – i.e. above LBF for shorts and vice versa for longs. In case the entry levels are not evenly spaced out, I might manually follow previous S/R to achieve that. I prefer even distributions to avoid putting too much weight on one level within a channel.
Similar to the entries, I will pick the pivot levels for exits, as well. The relevant targets should be at/below the LBF for shorts and vice versa for longs depending on your guts to hold the trades. Once the overall position (=swarm of orders) is in profit you can place a breakeven stop loss, if you like. I usually do this, when I have to step away from the computer. I don’t trail individual orders and aim to close swarms during one trading session to benefit from higher balances for the next opportunity.
The regressions and their standard errors give me a reasonable gauge for lot sizing – i.e. a std error of 15 pips, a capital of 10,000 and a setting of 0.5% would result in 0.33 lots or 3.30 dollars per pip on my account. I don’t use stops but average-in with the same lot size, because I am trading a channel bias with some temporary statistical relevance. There is no singular truth about direction, but the odd relevance of pivot levels, the average price of an order swarm and continuous risk exposure. Assuming the unlikely situation of a 6 sigma move (=the huge parallel shift in the size of a whole channel) from the first entry with 5 more re-entries, you’d be facing an overall negative float of 0.5%*(6*7)/2=10.5%. Since I track every order individually, that’s when I’d close that particular order with a loss of 3%. Unless there is a Brexit or SNB unpeg type of event with lasting tectonic shifts, there is usually enough volatility to unwind the swarms within a few days at max. I’d like to stress here, that I’m deliberately trading with more risk than others, because I’m in it to generate continuous income.
I’d be glad to answer your questions.