The unknown obvious: there's only one strategyThere's only one trading strategy, one way to trade and +inf number of ways (most of them are senseless) to model it & play around it.
How & why prices move is not a mathematical principle that can be explained with a set of logic.
It's the set of logical principles that can be modeled with mathematics.
These mathematical aka quantitative ways are numerous and generally offer a tradeoff between the computational needs and the resulting quality.
Take a look at my chart, you's see the weighted box plot that includes 80% of the data and weighted mean & standard deviations that also include 80% of the data. They're almost the same! As they should be, since the're modelling the same stuff. Box plot is a lil better since it's non-parametric (works well for all the distributions). WMA & WSTDEV are less on point, but cmon, easier to compute. And then you have the 1st degree model - weighted regression (WLSMA), that for some "unknown" reason sometimes matches the deviations that include 80% of the data?! Hard to compute tho, matrixes, vectorized ops..
Different ways, different tradeoffs, the same end, pick your poison & go.
Thing is it's all modelling, but the real underlying principles are much easier, and strangely, hard to automate 4 real, at least business wise. These things are very hard to algorithmize, and probably impossible to just calculate at all. The principles themselves are easy tho and are the same on any resolution:
1) levels are the places where it was/it is/it will be potentially or proved with evidence as cheap/expensive;
2) everything else in between these levels are buying/selling waves aka directional order flows;
Minding all that, there's only one "strategy" that will let you make the market better & earn money by doing so: buy @ potentially cheap & proved cheap, sell @ potentially expensive & proved expensive. All your momentums & mean reversions etc are ideologically the same things that allow you to do it. Everything else is a question of position sizing and gradual risk loading/offloading.
Now coming back to quantification of all this stuff & automation.
In terms of algorithmization, levels are the nightmare. their origins, positioning, clearing. You'll need to run numerous nested cycles on wide data and query databases nonstop in order to process it all, and you'll need to do it on all the resolutions you use. Waves are even more complicated, they start & end in particular places and levels affect it, they get exhausted & overridden. Wave starts/wave ends are based on levels, sometimes on higher resolutions, recursions are involved. Now imagine you're doing it on multiple assets in business environment. I don't even mentioned many absolutely deterministic & well defined judgmental calls that are made during borderline cases.
You can instead try to approximate it all using mathematics. Since the real original principles will not be reached anyways, the best we can do is to include all the information in our models and pick the formulas & methods that are as much coherent with the source as possible.
Formulas & methods are secondary. Regardless the methods, fancy formulas, what you call ML & AI these days, omg DSP adepts, bloody wavelets and ftts, etc etc etc, you can gain as much information from the data it as it is there.
Information is the main thing. The whole game if about information. Features are inherited from the fundamental particle of the market: a tick. Tho, we more interested in the 'tuple' of the 2 last ticks: current tick and the previous tick (wassup Markov).
1) Price. The actual sampled prices, calculated volume modes of every bar, HLC3, HL2, but never a Close lol;
2) Time. Can estimate the most prominent cycle and divide it by 2 / leave it alone;
3) Sequence matters. May be achieved via linear weighting of the data points;
4) Volume. Weighting by volume/ inferred volume;
5) Direction. Plus or minus? Then multiplied by volume? We might have overshoots due to negative weights tho. Another way?
You'll surely end up with something working.
^^ Funny thing tho, it's all extremely easy to do as an organic life form, you just scroll through different resolutions and see it all on the charts in a matter of seconds w/o any brain damage, without any approximations, without any data loss.
I think at this point you understand that there's absolute zero sense in using any chart studies if you trade 100% manually. If you don't I'm spamming the F button
ONE
RISK TO REWARD 📚 An Educational Write-up on How to Find ThisIntroduction:
This illustration explains the minimum Risk-To-Reward ratio needed based on your average win-rate while using a fixed % risk amount.
"Risk-To-Reward ratio": The ratio of what you stand to lose compared to win.
"Fixed % Risk": A static % amount of your total account balance at risk per trade.
"Fixed Dollar Risk": A static $ amount at risk per trade. Regardless of account size fluctuations.
"Win-rate": The % out of all trades that are winners.
Steps:
1. Before being able to determine what Risk-To-Reward is acceptable to use, you will need to create a baseline measurement of your strategy's performance.
2. To create this baseline, you will need to backtest your strategy and obtain its current average win-rate.
3. This can be done using your pre-determined entry logic with a fixed stop-loss/take-profit offset amount.
(Adjusting your entry logic prior to finishing a round of backtesting may produce skewed results. Do not "cherry-pick" trades as that will lead to false results.)
4. Based on the resulting average win-rate you can then find the minimum Risk-To-Reward ratio you should be using.
5. Backtest again using the more optimal Risk-To-Reward ratio and repeat this step until the most optimal backtest results are obtained.
Here is the formula for determining your Average win-rate after you have tallied the wins/losses of your backtest:
#W = Number of winning trades
#L = Number of losing trades
(#W / (#W + #L)) * 100 = your average win rate %
-----
Introduction to Fixed Dollar Risk:
We have found it common for people to use the logic of fixed dollar risk amounts when calculating win-rates needed to break even, but then to use a fixed % risk in practice.
This simple-to-make mistake can lead to account erosion over time due to the way compounding works.
The fixed dollar approach uses relatively simple math for breaking even as shown below.
Example:
3 losing trades followed by 1 winning trade using 1:3 risk-to-reward achieves breakeven (ignoring trading fees and slippage)
This risk-to-reward ratio itself implies the win-rate needed (lose $100 three times, win $300 once, you break even).
The fixed dollar amount risk doesn't deal with compounding. As such, its logic cannot be used for fixed %.
-----
Using Fixed Percentage Risk:
Fixed % uses a more complicated and less apparent method for calculating how to break even. As shown in our illustration, if you take three losses in a row you won’t break even after your next win.
Fixed % is always dealing with the same % of your current balance. So as your balance decreases, the total dollar amount risked is less, and the total dollar amount gained with each win is reduced.
Thus, strings of losses require additional wins compared to the fixed dollar approach.
The fixed % method ensures against account erosion by showing the minimum win-rate needed to use each risk-to-reward ratio.
MATH NOTE: We used a simplified method for finding the minimum win-rate to make this useful and generally applicable. Our method is based on a given risk-to-reward ratio and assumes the max number of losses in a row to produce a minimum win-rate, it does not factor in all different possible loss strings and their probability.
-----
WHY USE FIXED % !?:
The question one will have at this point is, "Why to use fixed % if it is so F'ing complicated!?"
The answer to that is simple. Despite being more complicated, fixed % is actually objectively better by almost every other measure.
With fixed % you generally perform better than fixed dollar during strings of losses and wins. As with fixed %, you lose less as you go down (because you only ever lose 1% of your balance), and you gain more as you go up (because of your winnings compounding).
Not only that, but you also perform better even when losses and wins are more scattered, as you can see on the chart below.
-----
Conclusion:
Fixed % is more complicated than fixed dollar... to say the least.
However , it is none-the-less superior in most instances.
Use the logic above while using fixed % risk, since if you use fixed dollar logic but use fixed % in practice you will underperform your theoretical results.
If there are any major flaws in our logic/approach please let us know in the comments as of course, we are looking to provide as accurate instructional writeups as possible!
MATIC/BTC & ONE/BTC & CELR/BTC Positive CorrelationAs you see in the chart, here we have a good positive correlation between these 3 charts. These are #Binance IEOs and move together. We have Specified 'Top' and 'bottom' with green and red arrows that are at a same time in these charts.
