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Trading System: Cliff Notes


1)Own no more than 20 *active* option positions

2)Own no more than 40 option positions total.

3)1% position size per option with 5 possible exceptions.

4)Those 5 exceptions may all be 2%. max 2 may be 3% and max 1 may be 4% positions.

5)Target max 50% of stock positions ~5 positions of 10% or less.

6)10% income position that is always on except sold to avoid margin.

7)Remaining capital with 1-5% in asset allocation options. (commodities, currency/cash, stocks/short VXX, bonds/income) plus possible 2% hedge.

8)Only take a trade where reward is 3 times the risk or more.

9)Monitor breadth to add more when it’s oversold or when it makes a strong breadth thrust off of oversold, add normally when it’s not overbought, and add proportional to rate of selling or slower when it’s overbought or trending down from overbought (until oversold signal).

*A position that falls 75% below it’s original value you basically write off as a loss.


-Watchlist is developed through OABOT’s top 400 and manually filtered from there to usually 20-60 names.

-Import watchlist into a spreadsheet with suggested stops and targets and current price

-Reward/risk will automatically update once it’s in the watchlist.

-Trading rules for entry that must follow the above portfolio rules and also an entry checklist which should also be using triggers.


1)As stocks are bought, input the stop, target, entry price into spreadsheet.

2)Since many of you are already trading, don’t worry about importing a large list of current holdings… just update the next trade until you phase out the old trades.

3)Stock must be below stop 5-10 minutes before trading close to trigger a sell.

4)Stock above target has a separate rules of waiting for the candle to close below prior candle low or failing to close above prior candle high Sell before the following candle’s close.

5)Which timeframe you use when stock is above target depends on condition but in general: With investments you may use monthly or weekly chart. With stock trades and options that have more than a week remaining til expiry you use daily chart. on options expiry week you might use a 1hr or 4hr chart. 1 day before options expiry you may use a 30 minute chart. On options expiry day you may use a 5m chart.

6)Generally update and check spreadsheet every hour. Also monitor watchlist stocks for purchase. On options expiry day check them every 10-15 minutes. One trick with that is if the stock is higher than the last time you checked, you don’t have to look at a chart.


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Option Trading Systems Part 3: Implied Odds

In trading systems there are the first set of expectations based upon the upside if you hit the target, and the probability of that upside measured against the expectation and probability of the downside. This was discussed using the metaphor of “pot odds” in Trading Systems part 1. But does it really end there? What about trades you close that neither hit the target NOR hit the full max downside? What about trades that exceed the upside target?

I believe you can structure a system and understand expectations with mathematics, but where things get fun is in estimations of math based upon conditions. Your read of the situation, the players involved, the possible alternative options, having contingency options and adapting to circumstance is what gives any game “character” and turns it less into a math equation and more into a balance of intuitive “feel” combined with a mathematical equation.

Back to the poker parallel, you may have pot odds to continue on a flush draw, but what about the value that comes in hitting the flush? If you know once you hit your flush that you can expect on average to get paid more and not lose any when you miss? More value. If you also can potentially win with an ace high if your opponent doesn’t put an additional bet in on a bluff? You may have more profitable situations than can be calculated by simple pot odds. As a result there my be situations even where “pot odds” doesn’t accurately describe the true upside.

When trading options, you are not trading a binary system. Even if you choose to cap your winnings by writing a call spread and sell the call at the strike price equal to the target price to cap your potential, you still have some trades that made less than the target amount but still make money, or trades that lose money but don’t lose the maximum. As such, just about anyone trading options is going to have to think beyond “pot odds”.

In trading it important to allow yourself to have that big payoff as a result of letting winners run beyond the target, particularly if there is little resistance once you get past said target. A good situation in an individual trade is if you have a clear volume pocket up to say $50 before substantial resistance but then the price history thins out above $52 all the way up to $60. If the stock’s upward momentum charges right through $50 and gets above 52, you now have new support and potentially could march to $60. Even though the trade plan called for $50, the system can be flexible and call for an audible and instead try to milk the trade for all you can. Even though you might think you can only get a small bet out of opponent you may pick up a tell that the card helped him, so you might try a check-raise to lure him to commit more chips. Fortunately, the “expected value” calculation mentioned in Trading Systems part 2 still is relevant as an AVERAGE when planning the system, but less so on individual trades.

There is a concept in statistics known as “variable change” that was popularized in the movie “21” about the MIT blackjack team. I will cover the details later, but basically by adapting to new information, you may be able to gain an edge by adjusting your decision as the trade plays out. In this case, “Variable change” is relevant because rather than apply a general baseline statistical data to what our expectations are based upon the average risk/reward of 3:1 that we target, or even say the R/R at $50 that we initially planned on the trade, we can take into account the most recent action of the stock and “call an audible” to maximize our results.

In blackjack “variable change” is more concrete as you can adjust to the “count” by calculating how your odds have changed as a result of several face cards being dealt already or several small cards being dealt. In poker implied odds can’t be known since the depend upon our opponent. In trading the upside and probability of hitting cannot be known with any sort of large sample size and small margin of error. Hence, it is more intuitive and up to the “read” of the individual. While that may seem sensitive to the individual trade and highly subjective which leaves room for mistakes, you can manage it such that the confidence level that “calling an audible” is more profitable than not is extremely high. Over time your skill and results may influence the profitability of the trading system, and your confidence will improve in your ability to correctly call an audible that adds value to the system.

The “implied odds” calculation is basically very much like pot odds since once a stock reaches the target price, you have a risk of continuing to hold plus a reward of continuing to hold. Once the expected value of holding no longer adds value,you can sell so as long as you keep in mind the overall context of the trading system must still be intact such that overall on average you reach your target often enough to offset losses and profit besides. Since you have hit the target, you will more actively manage the option and have an idea of under what conditions you will sell, and on average how much you lose when wrong by continuing to hold.

Where implied odds can get most confusing is in factoring it in before you start your trade, just as an intelligent poker player would not call on the flop without first intuitively considering the possible actions that may follow and the overall expected value as a result of betting on all streets. When trading a weekly option (or “yolo” as they are known around here), It often is easier to identify a price level whereby if price passes, the stock should run as those in a position get squeezed out. Often times yolo trades may have a strikeprice that is at the target and the reward is in getting beyond it and running as the shorts are squeezed out and past sellers look to get back in.

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Trading Systems Part 2: Accounting for Multiple Outcomes

In a basic “pot odds” calculation as described in the last post trading systems: pot odds and trading, if you can let your winners run so that you sell your option for 4 times the cost, netting 3 times your risk or 300%, you are getting 3:1 and thus only have to be right more than 1/4. (lose 3, win on 3 the 4th time and break even). However, options are more complicated than that. While you may be right or wrong on whether the stock goes to a price such that you earn 300%, the stock could reach the target earlier, in which case you may make more than 300%. Also, the timing could be late but direction still right, or the stock could gap up or run past the target and exceed your return. As a result, you might have a number of outcomes ranging between the extreme 1000% or so outlier trades to 0% depending on your ability to let the winner run and your tolerance for volatility. A simple pot odds calculation becomes tricky for determining whether or not the system is profitable, and how much. For this you can use “expected value”, but that really doesn’t grasp the expectancy of the system per trade over time. Nevertheless, since 1% risk represents a small amount of capital lost through volatility, it can still be looked at from the concept of how many 1% position trades are taken per year, and you can approximate that the growthrate that occurs in your portfolio is close to 1% of the expected value of the system as a decent starting point. With such a position size, you can take more trades on at once and as a result the gains can compound faster. There is still some correlation (a LOT of correlation during a significant correlated selloff), but by diversifying trades over multiple time frames and spreading out your purcahses and using the occasional hedge, you can mitigate the cost of volatility at very little cost to your overall results.

So let’s get into an example. Say you average out all your trades over 100%, You average all those over 30%, all those between -30 and 30% and all those from -30 to -90% and then all trades that lose more than 90%. That gives you 5 averages that can define your system in the total average of “large wins” “moderate wins” “break even” “salvaged premium” and “full loss”. The frequency of each event determines a number of trades out of all trades. Simply divide the number of trades by all trades for each of the 5 and you have an estimated probability of the return. Average the return of all trades in each category to get the average return.

From here I actually like to model my results as opposed to using expected value, but let’s just give an example:

Last year’s 2013 results before I booked a couple of large wins in TWTR and TSLA were

17% of trades averaged 294%, 20% of trades averaged 53%, 15.8% of trades averaged -1% 23.2% of trades averaged -63% and 24% of trades I rounded down to the full 100% loss.

To do an “expected value” calculation, you simply multiply the probability of each event by the return and then add them together. So (.17*2.94)+(.2*.53)+(.158*-.01)+(.232*-.632)+(.24*-1)=.217596=~21.76% gain per option trade. By letting the winners run in 2014 from Jan until Jun before a few large wins in AMZN and PCLN and others, this yield was just above 40% but it was also substantially helped by a few large outliers well over 500% (GMCR,AAPL,etc) . So how does that translate to portfolio growth? There is an easy calculation that can be done if the position size is small.

Let’s put it at 1% risk or 1% of .2176 expectation=.002176% per trade. In other words, we multiply at a factor of 1+.002176 per trade. (1.002176^X)-1=annualized rate of return where X is the number of 1% trades you can place with this system in a year.  Make 300 trades and you are looking at about a 1.002176^300=~1.919-1=~.92=92% annualized rate of return.

The smaller the position size, the more consistent the system, the lower the correlation between trades, and the greater the edge, the more accurate the estimation is and typically the more consistent the results are with the expectation. However, keep in mind the relationship of return and risk as you look at how it becomes less accurate with increased position size. The assumption that increasing the risk will proportionally increase the volatility and the reward assumes a linear relationship which is not true. Instead the relationship looks as follows:


Nevertheless, we can use a monte carlo simulation to pull a random number between 0 and 1. Since there is a 17% chance of the upper result if it pulls a 0.17 or less, this corresponds to the 294% return result occuring, where as a 20% chance of a 53% return corresponds to a .17 to .37 being pulled and so on. That can allow us to randomly pull the results of a thousand trades, and then through a monte carlo simulation tool we can simulate thousands of trials of 1000 trades and model any particular result or formula as a consequence of each result to get the intended information we need and reflect the results we want. Just by using the average return and ignoring the substantial probability of effective ruin

At 1% with ZERO fees, we get an average return of 91.35% compared to our expected 91.9%

With 2% with zero fees we get an average return of 265.7% compared to an expected ((2*0.002176)+1)^300=268%. Being able to place the same volume of trades as the risk gets too much larger becomes increasingly difficult and increases the correlation risk that is not modeled here but will negatively effect returns. As we increase the position size the difference between the average simulated return and the calculated return grow as the simulation is less and less close to the calculated returns due to losses from volatility. The other thing is, as bet size increased it becomes increasingly difficult to place the same number of trades per year.

What this growing difference does not show is the increasing skew of results. In other words, if 1000 traders were to trade this system over 300 trades at a fixed position size, a smaller and smaller percentage of traders would have a greater and greater result as position size increased. The magnitude of the outliers would increase which would skew the AVERAGE expected results. The opposite side of it would be an increasing probability of poor results as well.

The kelly criterion is a risk management based mathematical formula to assess risk management to maximize gain. Effectively, the more you increase the position size the more you turn it into a lotto ticket, until you eventually take your skill edge out of it and your results actually begin to decline and eventually even become negative if you get too carried away. Even if you are adjusting the percentage risked, you should never go over the full kelly, and since you don’t have an unlimited amount of time to recover the drawdown, and you can only place a few hundred trades per year with overlapping correlation, and there is greater uncertainty… I would absolutely drastically reduce the bet from the kelly. Calculating the “kelly” is a process that has many applications and is another story for another time.

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