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--ES Results-- Per Contract Profit: +$66,352 Max DD: -$415 Trades: 1342 Percent Profitable: 86.96 PF 5.36 Sharp 1.47 Average Trade: $49.44

--6E Results-- Per Contract Profit: +$130,160 Max DD: -$782 Trades: 1513 Percent Profitable: 88.63% PF 6.00 Sharp 3.18 Average Trade: $86.03

The following posts will outline detailed statistics on the 3 markets I will initially be trading. ES, CL, and 6E. The statistics are from 2009 until present as this was the "out of sample" period. --CL Results-- Per Contract Profit: +$229,220 Max DD: -$655 Trades: 1510 Percent...

The strategy trades based on 1min bars. The strategy is restricted to opening new positions only between 9:35 to 3:00 EST

yes 4.15 EST will be considered EOD. However, typically the system will be out of the trade by 3:45 EST. My trading machine is hosted at my office which is next door to a datacenter. I have backup power here as well as multiple fiber connections feeding the datacenter. As a fallback...

The system makes all of the trades automatically. All I do is turn it on in the AM. After 2:00 central it stops taking new trades and closes out any remaining open before market close.

I have been developing automated systems since 2004

I need to look into it more and perhaps even do some tweaking, but I may replace NQ with 6E. Gives me slightly better diversification even though its a slight hit to the results.

I am trading with IB

When backtesting your fills are simulated as of the current market price. So with ES for example, the last trade may have been 1225.25, but you do not know if that price was the bid or the ask. If you go to buy and the 1225.25 was at the ask, then you get filled at 1225.25 with your market...

To remove all doubt in my mind before turning this on live, I decided to trade against a few more instruments to determine if the edge would persist: ZB: 6,281 trades, 68.23% profitable, +$183,985 per contract over 10 years.. Significantly lower performance than CL,ES, or NQ.. but still...

Each neural network has 15 inputs, 7 neurons on a single hidden layer and 1 output. 1) Ave no of trades/day/symbol It takes 2.2 trades per day per symbol 2) Ave hold time Hold time is under 1 hour 3) Calmar Ratio (Annualized Return / Max Drawdown) I am not familiar with this ratio...

In many respects it appears "too good". I have done two sanity checks over the past month, each lasting only a few days. #1 I let it run on SIM account for a few days then compared those results/fills with backtesting that same period. Matched up very closely. #2 I let it run for 1 day...

The strategy is designed to protect as much capital as possible. Every trade has a "worse" case stop of around 20 ticks. This very rarely gets hit. When trading ES, NQ, and CL all as a portfolio, the strategy has less than 1% chance of any given week to be a loser. With that said, I am sure...

The strategy consists of around 20 neural networks. Each NN was trained against ES data from 2000-2009. I have used neural networks in the past with sub-par results. However, typically as soon as I test out of sample data the strategy falls apart. This time was different. I decided to throw out...

The goal of this journal is simple. To prove that it's possible to have huge returns from a 100% automated system. After years of frustration and "almost" moments, I nearly gave up. A few months ago a light bulb went off. I had an idea, one that I had many times before, but this time was...

What are the best futures for day trading? Are ES and CL the only two viable options as far as daily volume?

How soon after the peak/valley do you need to have identified that was in-fact a peak/valley?

What do you mean by an OTR range-based rule?

When developing an automated strategy, what I find the most challenging is exits. Exiting when an oscillator becomes oversold seems to work occasionally. Generally providing a small boost. However, it misses the big runs and doe s nothing for getting out a losing trades. What are some "exit"...