Bitcoin Exchange Bitcoin Trading - CEX.IO

After searching around I just found a lot of sketchy stuff and I‘m searching for people that actively (day-) trades, trades options and futures with Bitcoins. Also I‘m searching good, quality content on how to improve in trading bitcoins.

https://www.tradingview.com/script/4J5psNDo-A-Physicist-s-Bitcoin-Trading-Strategy/
3. Backtest Results
Backtest results demonstrate significant outperformance over buy-and-hold . The default parameters of the strategy/indicator have been set by the author to achieve maximum (or, close to maximum) outperformance on backtests executed on the BTCUSD ( Bitcoin ) chart. However, significant outperformance over buy-and-hold is still easily achievable using non-default parameters. Basically, as long as the parameters are set to adequately capture the full character of the market, significant outperformance on backtests is achievable and is quite easy. In fact, after some experimentation, it seems as if underperformance hardly achievable and requires deliberately setting the parameters illogically (e.g. setting one parameter of the slow indicator faster than the fast indicator). In the interest of providing a quality product to the user, suggestions and guidelines for parameter settings are provided in section (6). Finally, some metrics of the strategy's outperformance on the BTCUSD chart are listed below, both for the default (optimal) parameters as well as for a random sample of parameter settings that adhere to the guidelines set forth in section (6).
Using the default parameters, relative to buy-and-hold strategy, backtested from August 2011 to August 2020,

• Total cumulative outperformance (total return of strategy minus total return of buy-n-hold): 13,000,000%.
• Rolling 1-year outperformance: mean 318%, median 84%, 1st quartile 55%, 3rd quartile, 430%.
• Rolling 1-month outperformance: mean 2.8% (annualized, 39%), median -2.1%, 1st quartile -7.7%, 3rd quartile 13.2%, 10th percentile -13.9%, 90th percentile 24.5%.

Using the default parameters, relative to buy-and-hold strategy, during specific periods,

• Cumulative outperformance during the past year (August 2019-August 2020): 37%.
• 12/17/2016 - 12/17/2017 (2017 bull market) absolute performance of 2563% vs buy-n-hold absolute performance of 2385%
• 11/29/2012 - 11/29/2013 (2013 bull market) absolute performance of 14033% vs buy-n-hold absolute performance of 9247%

Using a random sample (n=20) of combinations of parameter settings that adhere to the guidelines outlined in section (6), relative to buy-and-hold strategy, backtested from August 2011 to August 2020,

• Average total cumulative outperformance, from August 2011 to August 2020: 2,000,000%.
• Median total cumulative outperformance, from August 2011 to August 2020: 1,000,000%.

EDIT (because apparently not everybody bothers to read the strategy's description):
7. General Remarks About the Indicator
Other than some exponential moving averages, no traditional technical indicators or technical analysis tools are employed in this strategy. No MACD , no RSI , no CMF , no Bollinger bands , parabolic SARs, Ichimoku clouds , hoosawatsits, XYZs, ABCs, whatarethese. No tea leaves can be found in this strategy, only mathematics. It is in the nature of the underlying math formula, from which the indicator is produced, to quickly identify trend changes.
8. Remarks About Expectations of Future Results and About Backtesting
8.1. In General As it's been stated in many prospectuses and marketing literature, "past performance is no guarantee of future results." Backtest results are retrospective, and hindsight is 20/20. Therefore, no guarantee can, nor should, be expressed by me or anybody else who is selling a financial product (unless you have a money printer, like the Federal Reserve does).
8.2. Regarding This Strategy No guarantee of future results using this strategy is expressed by the author, not now nor at any time in the future.
With that written, the author is free to express his own expectations and opinions based on his intimate knowledge of how the indicator works, and the author will take that liberty by writing the following: As described in section (7), this trading strategy does not include any traditional technical indicators or TA tools (other than smoothing EMAs). Instead, this strategy is based on a principle that does not change, it employs a complex indicator that is based on a math formula that does not change, and it places trades based on five simple rules that do not change. And, as described in section (2.1), the indicator is designed to capture the full character of the market, from a macro/global scope down to a micro/local scope. Additionally, as described in section (3), outperformance of the market for which this strategy was intended during backtesting does not depend on luckily setting the parameters "just right." In fact, all random combinations of parameter settings that followed the guidelines outperformed the intended market in backtests. Additionally, no parameters are included within the underlying math formula from which the indicator is produced; it is not as if the formula contains a "5" and future outperformance would depend on that "5" being a "6" instead. And, again as described, it is in the nature of the formula to quickly identify trend changes. Therefore, it is the opinion of the author that the outperformance of this strategy in backtesting is directly attributable to the fundamental nature of the math formula from which the indicator is produced. As such, it is also the opinion of the author that continued outperformance by using this strategy, applied to the crypto ( Bitcoin ) market, is likely, given that the parameter settings are set reasonably and in accordance with the guidelines. The author does not, however, expect future outperformance of this strategy to match or exceed the outperformance observed in backtests using the default parameters, i.e. it probably won't outperform by anything close to 13,000,000% during the next 9 years.
Additionally, based on the rolling 1-month outperformance data listed in section (3), expectations of short-term outperformance should be kept low; the median 1-month outperformance was -2%, so it's basically a 50/50 chance that any significant outperformance is seen in any given month. The true strength of this strategy is to be out of the market during large, sharp declines and capitalizing on the opportunities presented at the bottom of those declines by buying the dip. Given that such price action does not happen every month, outperformance in the initial months of use is approximately as likely as underperformance.

https://github.com/jesse-ai/jesse
I know many of you are already familiar with other projects such as Gekko and Freqtrade, etc and would like to know why I started Jesse instead of using them. So here are a few reasons:

• Simple syntax for defining strategies. I have years of experience and a deep understanding of frameworks such as Laravel and Vuejs which are popular for their simplicity. I designed Jesse the same way. So #1 thing that you'll like about Jesse is how easy it makes it for you turn a strategy idea into actual code.
• Support for using multiple timeframes and symbols in a single strategy. I haven't seen any other project that can offer this without the look-ahead bias. Jesse does that.
• Trading more than one pair at the same time. Jesse allows doing this. I designed it using Routes, which is a concept borrowed from web development.
• It is accurate. This part was a real pain. I did it tho. Through hundreds of unit tests.
• Provides tools for getting and manipulating data in Jupyter notebooks.

Example Strategy

Here is an example of a profitable strategy that shows the usage of TA and the syntax of the framework:

from jesse.strategies import Strategy import jesse.indicators as ta from jesse import utils class SampleTrendFollowing(Strategy): @property def long_ema(self): return ta.ema(self.candles, 50) @property def short_ema(self): return ta.ema(self.candles, 21) @property def atr(self): return ta.atr(self.candles) def should_long(self) -> bool: return self.short_ema > self.long_ema def should_short(self) -> bool: return self.short_ema < self.long_ema def should_cancel(self) -> bool: return True def go_long(self): entry = self.price stop = entry - 3*self.atr qty = utils.risk_to_qty(self.capital, 3, entry, stop) profit_target = entry + 5*self.atr self.buy = qty, entry self.stop_loss = qty, stop self.take_profit = qty, profit_target def go_short(self): entry = self.price stop = entry + 3 * self.atr qty = utils.risk_to_qty(self.capital, 3, entry, stop) profit_target = entry - 5 * self.atr self.sell = qty, entry self.stop_loss = qty, stop self.take_profit = qty, profit_target 

And here is the backtested results:

 CANDLES | ----------------------+-------------------------- period | 365 days (12.17 months) starting-ending date | 2019-01-01 => 2020-01-01 exchange | symbol | timeframe | strategy | DNA ------------+----------+-------------+----------------------+------- Bitfinex | BTCUSD | 6h | SampleTrendFollowing | Executing simulation... [####################################] 100% Executed backtest simulation in: 22.61 seconds METRICS | ---------------------------------+------------------------------------- Total Closed Trades | 24 Total Net Profit | 3991.39 (39.91%) Starting => Finishing Balance | 10000 => 14063.47 Total Open Trades | 1 Open PL | 97.15 Total Paid Fees | 498.22 Max Drawdown | -15.9% Sharpe Ratio | 1.18 Annual Return | 26.46% Expectancy | 166.31 (1.66%) Avg Win | Avg Loss | 558.76 | 383.12 Ratio Avg Win / Avg Loss | 1.46 Percent Profitable | 58% Longs | Shorts | 67% | 33% Avg Holding Time | 2.0 weeks, 12.0 hours, 53.0 minutes Winning Trades Avg Holding Time | 1.0 week, 4.0 days, 6.0 hours Losing Trades Avg Holding Time | 2.0 weeks, 5.0 days, 2.0 hours 

Above example was just an example! Please don't compare it to hodling; or you know, do it! It'll be fun.
This project took me ~2 years. I did it for my own trading needs and I am very happy with the results. Now that I'm releasing it to the public, I hope you guys like it too.
Cheers

Is it legal to trade XBTUSD pairs on bitmex? Or we are allowed only to buy and sell with INR like in forex where only INR pairs are legal. Where can I find information on this? Any govt sources ?

Just kidding. That's impossible because no one controls the network. Keep hodling.

Ever wondered how bitcoin trading and investment works? if you want to learn how to earn through bitcoin trading and mining please send me a message. #bitcoin #cryptocurrency #binaryoptions #blockchain

Trading refers to the exchange of any goods, assets, or services. If you are selling something at a higher price than what you purchase it at is known as trading. Trading is not a basic exchange of goods and services as the major difference is that trading is done with the objective of earning some […]

I’m using Coinbase pro right now but I noticed that it takes some time for an order, 5 minutes ago I placed an order and it’s still “on order” which means that the app is useless for day trading. Also fees are quite bad so id like something cheaper