Understanding Your Statistical Edge STATISTICAL EDGE
A player's advantage in a game of chance that ensures favorable outcomes over the long run is referred to as a statistical edge. Think about a situation where a coin is rigged so that one side has a 51% chance of dropping heads while the other has a 49% chance. When a player wins, they are paid 1, and when they lose, their opponent is paid 1.
This establishes the rules of a game of chance, such as the likelihood of winning and losing, the reward for winning, and the penalty for losing. We may calculate the expectation using these parameters, which determines whether or not one has a statistical edge.
If the expectation is higher than 0, the player has a statistical advantage that could result in long-term financial success. The player who wins the coin game 51% of the time will make money in the long run because the expectation in this example is 0.02.
The metrics of a trading strategy are similar; the win rate, which measures the likelihood of winning, and the average Reward to Risk Ratio (RRR), which measures the average profit divided by the average loss of your trades, are both used. Consider a system that wins 40% of the time and has an RRR of 2, which means that winning is worth twice as much as losing. In this scenario, you risk 1R on each trade, and out of 100 trades, you win 40 and lose 60.
Your expectancy is then calculated by multiplying the number of winning trades by the reward (2R) and subtracting the number of losing trades by the risk (1R), resulting in a profit of 20R over 100 trades. Therefore, your expectancy is $0.2, meaning that for every 0.1 you risk on a trade, you earn $0.2 on average.
A successful approach, however, will fall short if proper risk management practices are not followed. A trading strategy is a tool, but risk management is what allows you to profit.
WINRATE & LOSES
The chart above displays your chances of having consecutive loses based win rate. As seen in chart above even if your trading method is 60% successful, there is still a 70% risk that you will suffer four consecutive losses. You have a greater than 50% risk of suffering eight consecutive losses if your strategy only succeeds 40% of the time. Knowing these numbers is very important as it helps with your psychology when you find yourself losing a lots of trades and you're questioning your strategy.
IMPROVING YOUR RISK TO REWARD
One way to improve your reward on trades is by using your MAE and MFE metrics to determine stops and take profit.
A trader's stop-loss and take-profit levels can be determined using the metrics MAE (Maximum Adverse Excursion) and MFE (Maximum Favorable Excursion).
No matter whether a trade is profitable or not, MAE calculates the maximum drawdown that can occur from the deal's highest point to its lowest position. However, regardless of whether a transaction is ultimately lucrative or not, MFE evaluates the highest profit a trade can achieve from its entry point.
In the above image we can see MAE calculations on trades. We can see that of all the trades taken the trades that have the best performance (above the blue line) are the trades with the least amount of drawdown. This information can help a trader determine at what point he/she should be closing trades if not running in their direction.
Trading professionals can better understand the behavior of the market and set more sensible stop-loss and take-profit levels by looking at the MAE and MFE of their transactions.
For instance, a trader may want to tighten their stop-loss level to reduce potential losses if they notice that their trades frequently encounter a significant MAE before eventually reaching their take-profit level. On the other hand, if a trader notices that their trades frequently encounter a significant MFE before being ultimately stopped out at a loss, they might want to think about establishing a broader stop-loss level to give their trades more breathing room. Overall, using MAE and MFE can assist traders in better understanding the advantages and disadvantages of their trading strategy and in adjusting their risk management plan accordingly.
Stats
Q&As: non-market dataThere's some curious personalities that trade (at least claim to trade) based on news, fundamental metrics, alt data n stuff. I don't mean invest, I mean trade. Well that looks like a skill to be proud off, superstimuli always feels cool aye? Good thing tho there no real reason in doing it all.
The most precise term to explain non-market data is, well, everything that ain't have a direct involvement with what happens inside the order matching servers of a given exchange.
So open interest is in fact a great example of non-market data.
The one & only real purpose for using all this data is to know (not to guess/predict/forecast, not to even anticipate), but to understand when the ACTION is going to happen. If you think deeper, ultimately it's all about asset selection to satisfy whatever purpose you got. if you ever got caught yourself feeling fooled when media release a bad info but prices go up, or media release a good info but prices go down, it's ok. It doesn't work that way, direction of prices can't be affected this way. Direction of prices is the result of how buyers meet sellers which is based on +inf number of factors, where a non-market data is simply just one of these +inf factors. It exclusively provokes action, meat, hype, momentum, volatility, whatever you call it. What's happening is that things start to happen very fast. Without a trigger event, the trading activity would've been the same, it just would've take longer to unwind. News don't change the structure, they make it all happen faster, that's it.
Examples of non-market data that can be used to expect action:
1) Trading schedule, eg the US, EU opening times;
2) Economic releases;
3) Commitment of traders reports;
4) Significant news;
5) Changes in yield curves;
6) "Fundamental" stock data;
7) Open interest;
8) etc etc etc
One really important thing to add is that, just like trading activity is understood in context (other resolutions), sizing also includes context (equity control, market impact), the same way every non-market data event lives in the context (previous releases, other releases, overall economy). You're interesting not in a new per se, but rather in what does it mean in the world. For example, inflation reports don't mean much when the rates are low, but when the rates are high, they trigger significant activity.
That's the area where statistical learning, automated learning, "machine" learning, 'Really' starts to make sense business-wise. The ultimate goal is to create a system that will process every kind of data you have (NLP and TDA should help) and output the tickers with raising/already risen levels of interest.
Quick notes about Inverted Head and ShouldersHello dear friends,
this is more of a study than a chart.
Here is what i did:
* I sampled 16 IHS that showed over the past year and were visible on the 4hr chart.
* I took in consideration sloppy patterns but ignored the ones that didn't complete or patterns that were too small or too big to discern on the 4hr.
* targets are calculated roughly based on the closest fib as result, some targets have fallen slightly above or below that number.
* By target i mean the peak price that was reached after pattern completion and within the timeframe equal to the time length of the pattern itself, and before reached a lower point than the low tip of the right shoulder
* The outcome post pattern defines what happens after that pattern has reached its target within the calculated timeframe. Lower means the price dropped below the right shoulder lowest point, higher means the price continued climbing higher before any drop
* The way i calculate IHS targets might be a bit unusual, but it works well for me. I calculate the length of the head from its lowest point to the meeting point with the right shoulder. Target calculations are a fib percent of that height.
* Mind you, this is a quick study, nothing bulletproof or scientifically sound.
And here are my key findings:
1. Out of 16, 3 failed, 7 reached 100% or above
2. the average success score is a retrace of 67% of the head size within the time length equal to the time length it took the pattern to form
3. 50% of the time, the price dropped lower than the pattern right shoulder or more after reach the pattern target
Details:
/
a. 50% / lower
b. 61.8% / higher
c. 100% / higher
d. 23.6% / lower
e. 100% / higher
f. 100% / higher
g. 0% / lower
e. 0% / lower
f. 23.6% / higher
g. 100% / lower
h. 78.6% / higher
i. 161% / lower
j. 23.6% / higher
k. 161% / lower
l. 0%/ lower
m. 100% / higher
Again please take these findings with a grain of salt, this is by no mean super accurate and can be prone to error. But as a broad overview, hope you'll find this insightful.