100% winrate trading strategy open sourced?(tradingview.com)
tradingview.com
100% winrate trading strategy open sourced?
https://www.tradingview.com/script/XnvfXLPF-NoNonsense-Forex-high-timeframe-trading-absurd-NON-REPAINTING/
13 comments
Devising a set of parameters that backtests 100% means nothing - there are an infinite number of parameters and models (or more accurately, algorithms) that can backtest any data set like this 100%.
In that sense, the math and "indicators" are just an obfuscated, custom-fit version of another algorithm that backtests 100% - if price[now] > price[now+1] -> sell else -> buy.
Rather than looking up the prices via array in the data, clearly "cheating", a set of parameters is hunted down that encodes that relationship in a number space. It doesn't mean anything more than the meme: 79 beers - your age + 40 dollars = the year you were born (which backtests perfectly as long as you were born after 1900).
That said, by all means, keep running the algorithm on new data every day and see how long it stays perfect. I'd be interested in the results.
In that sense, the math and "indicators" are just an obfuscated, custom-fit version of another algorithm that backtests 100% - if price[now] > price[now+1] -> sell else -> buy.
Rather than looking up the prices via array in the data, clearly "cheating", a set of parameters is hunted down that encodes that relationship in a number space. It doesn't mean anything more than the meme: 79 beers - your age + 40 dollars = the year you were born (which backtests perfectly as long as you were born after 1900).
That said, by all means, keep running the algorithm on new data every day and see how long it stays perfect. I'd be interested in the results.
I just did a quick dive into forex trading online to see what it would take to get this setup and... is it just me or are the websites and companies that run in this space the sketchiest things ever? I definitely do not trust any of these sites with the minimum deposits they are asking for.
Just wait until somebody offers you "signals"... hahaha
Hmmm, so I took this source code, compiled and it is still 100%.
I mean - the strat was published 8th of November, and you could say it was overfitted. But it continues to run. Since 8th of November it closed only 1 trade, but also $500+ profit. (second is still going, but currently profitable).
I mean - the strat was published 8th of November, and you could say it was overfitted. But it continues to run. Since 8th of November it closed only 1 trade, but also $500+ profit. (second is still going, but currently profitable).
TL;DR Title is misleading clickbait but the semi-interesting part is:
> Backtesting results [of the backtesting strategy referenced] look absurd: 100% profitable. But if you change any of the many parameters in the Settings popup, they will turn into disaster. It means, the rules of this strategy are very fragile. Don't trade this! Remember about backtesting rule #1: past results do not guarantee success in the future.
> Backtesting results [of the backtesting strategy referenced] look absurd: 100% profitable. But if you change any of the many parameters in the Settings popup, they will turn into disaster. It means, the rules of this strategy are very fragile. Don't trade this! Remember about backtesting rule #1: past results do not guarantee success in the future.
I’ve always wondered about this. Of course back-testing a specific timeframe doesn’t mean anything. But if I backtest a strategy on multiple timeframes in a sliding window fashion and am always profitable - doesn’t that mean anything? Would you still say that past results do not guarantee success in the future?
It still just means you're finding parameters that match some subset of the relationships present in the data as a whole.
All sliding the window does is discover the parameters that work for the whole data set in chunks - it is an artificial distinction. It still regresses to: you've found some number space generated by some function that matches some percentage of the numerical relationships (correlations) present in the data.
It's circular reasoning because during creating the parameters you're testing it on the "future" data. It only "guarantees" success in the "future" because you discarded all the parameters that didn't work in the "future". No different from writing a model that uses the S&P 500 price "parameters" between 250 and 1000 and back-testing it on data from 1950-1996.
The only way to prove your algorithm's robustness is to generate random data and test it on that. Once you've tested against every one of the infinite possible realities of a single time window, then you can rightly assert that past results have guaranteed success in the future. Hint: it's impossible, but the random data testing is actually the correct technique to test algorithms at scale.
Back-testing on historical data is like a footnote compared to the thesis simulation can generate - the only value it contains is correlating relationships between market data and external variables not present in the numbers. Back-testing to tune an algorithm based purely on the numbers in the data is just an exercise in quantified hind-sight bias.
All sliding the window does is discover the parameters that work for the whole data set in chunks - it is an artificial distinction. It still regresses to: you've found some number space generated by some function that matches some percentage of the numerical relationships (correlations) present in the data.
It's circular reasoning because during creating the parameters you're testing it on the "future" data. It only "guarantees" success in the "future" because you discarded all the parameters that didn't work in the "future". No different from writing a model that uses the S&P 500 price "parameters" between 250 and 1000 and back-testing it on data from 1950-1996.
The only way to prove your algorithm's robustness is to generate random data and test it on that. Once you've tested against every one of the infinite possible realities of a single time window, then you can rightly assert that past results have guaranteed success in the future. Hint: it's impossible, but the random data testing is actually the correct technique to test algorithms at scale.
Back-testing on historical data is like a footnote compared to the thesis simulation can generate - the only value it contains is correlating relationships between market data and external variables not present in the numbers. Back-testing to tune an algorithm based purely on the numbers in the data is just an exercise in quantified hind-sight bias.
> The only way to prove your algorithm's robustness is to generate random data and test it on that.
I would never to that. This algorithm appears to have worked well on EURUSD daily timeframe candlesticks data. It would be ridiculous to assume that it can work well on a random set of data, like global average temperatures daily or rate of births worldwide. Or even prices of oil or other currency pair.
I would never to that. This algorithm appears to have worked well on EURUSD daily timeframe candlesticks data. It would be ridiculous to assume that it can work well on a random set of data, like global average temperatures daily or rate of births worldwide. Or even prices of oil or other currency pair.
"Random data" didn't mean a random data set from a different domain. It meant random data from the same domain - simulated price/volume data within a reasonable range. If it can't work well on that, then it isn't a trading algorithm, it is a glorified fit curve.
> It meant random data from the same domain - simulated price/volume data within a reasonable range
How do you know what is a reasonable range without hypothesis on the price distribution ?
Where does these hypothesis comes from ? historical data ?
So.. is that really valid ?
How do you know what is a reasonable range without hypothesis on the price distribution ?
Where does these hypothesis comes from ? historical data ?
So.. is that really valid ?
yes. You know the lower bound on price is 0, and the upper bound of infinity is probably of no practical value, so you can pick something like 10 or 100x max all-time. Volume is the same, 0 to infinity, but again you can pick a distribution that is much (10-100x) wider than the real one. The wider the better, as it will better uncover tail risks and payoffs for highly unusual or atypical events (see Taleb, Black Swan, etc.)
I'm not making this up - this is how model testing is actually done, in multiple domains. Simulation is a reason banks, HFTs, hedge funds, etc. use massive compute infrastructure - doing it the right way, with many millions of plausible data sets, requires orders of magnitude more computing resources than back-testing on one data set that just happens to represent one way things could have played out (i.e. reality).
Thinking that one historical data-set is somehow special (in itself, without context) is largely a delusion. In fact you can generate near perfect historically accurate price charts just using a random walk algorithm seeded with an opening price.
I'm not making this up - this is how model testing is actually done, in multiple domains. Simulation is a reason banks, HFTs, hedge funds, etc. use massive compute infrastructure - doing it the right way, with many millions of plausible data sets, requires orders of magnitude more computing resources than back-testing on one data set that just happens to represent one way things could have played out (i.e. reality).
Thinking that one historical data-set is somehow special (in itself, without context) is largely a delusion. In fact you can generate near perfect historically accurate price charts just using a random walk algorithm seeded with an opening price.
Consider if your trading algorithm simply searched the history for that sliding window of data and then presented the following data as it's "prediction" -- it's utterly useless. This is the function of a compressor rather than a predictor.
Come on, there's nothing to be seen here. Hyperparameters were handpicked to result in 100% profit on a certain timeframe.
And the title is misleading.