Bitcoin 30m Swing Trader Long/Short Strategy

Intro
I want to share the results of my passionate hobby and the unstoppable chase for a profitable automated trading strategy. It has been created with the intention of trading only Bitcoin. Altcoins are not interesting for me, as I have discovered lots of issues with finding the right parameter values for experiencing a good performance. As altcoins typically follow the trend of bitcoin and characteristically have a high volatility that may cause stop-hunts, I decided to not over complicate this project. I was just aiming for a profitable trading strategy with an acceptable drawdown and enough confidence by a statistically significant number of trades beside a wide backtesting timespan (credits going out to TradingView: Deep Backtesting).
Total time spent on this is approximately 2 years.

Indicators used

• RSI: Used for entries and trend reversal spots
  
• MACD: Used for entry and exit optimiziation
  
• ATR: Used for dynamic offsets in trend definition indicator
  
• Custom trend indicator: Self-made indicator, based on simple price action of higher timeframes using pivot points to find support and resistance zones that have formerly been created
  

Strategy parameters
I have reduced the total parameters used to just a few. It took lots of working hours to find appropriate values along the trading algorithm and I don’t want to overcomplicate it to you.
This strategy is for those, who have been looking for a working strategy. No DIY kit.
Feel free to adapt Take profit or stop loss targets. But it’s not recommended to do so.

How it works

• Entries:
  I started with a kind of template that I have been using for strategies for a long time. This includes how to find the right Entries during a trend as well as spotting trend reverse opportunities. Here I combine simple indicators like RSI and MACD beside necessary trend conditions. If a target RSI Value is hit, it will enter a trade, after MACD histogram has stopped to fall/rise. Depends on long/short. While we are in a trade and trend reversed, it waits for a specific RSI target level to be hit, to reverse the trade. As simple as it is, it closes the open one and starts a trade in other direction.
  
  
• Micro trend:
  It starts to get more interesting when it comes to trend recognition, as it forms the core of the strategy and discovering appropriate values for it has been very hard. The final trend variable is defined by the responses over higher timeframes of my self-made trend indicator. Executed on the current timeframe, the trend indicator is quite interesting. But for a automated trading strategy it is necessary to deviate trading instructions from higher timeframes trends.
  
  
• Macro trend:
  The same process that happens for micro trend is also applied with much higher timeframes, like 3D or weekly. The basic assumption is, that if we are in a bull or bear run, where retail investors are flooding the markets, we are increasing our take profit targets respectively. This way we can catch bigger moves in bigger trends.
  
  
• Exits:
  Closing a trade generally happens when a TP target (in %) is hit, or the SL (in %) is hit. The strategy has a special treatment with SL’s. After it happens, the strategy is more careful about market conditions and typically waits for a countertrade. The third way of closing a trade has already been mentioned: the reverse trades. They happen during choppy market conditions. The strategy has also special awareness here and tracks, if reverse trades start to happen more often. After a while, it starts to be more restrictive in opening new reverse trades.
  

Performance

• Capabilities and limitations:
  As I have already mentioned the strategy is only optimized for bitcoin (Perpetual Futures). This does not mean, it can not be used on other markets, because the algorithm itself is universal appliable. A very hard task was about finding the right parameter values for the strategy performing like this. If you have a special wish to configure this strategy for a specific market, DM me. The strategy has been tested with different configurations on the following timeframes: 30, 15, 10, 5, 1. I have decided to publish the one for 30m TF, because its performance simply convinced me.
  
  
• Repainting:
  It has been tested lots of times against repainting.
  
  
• Confidence:
  The total backtesting performance reaches out to 2019-09-08. So the strategy has been managing to be successful since then, but this does not guarantee that the logic, this strategy follows, is going to continue this level in future.
  
  
• Commission:
  The algorithm is configured with 0.04% commission per trade, as it is on Binance (for Future Market orders).
  
  
• Ordersize:
  Its totally up to you, how much of your total equity should be traded. Nevertheless, I would personally recommend to not exceed 50% ordersize of your equity with this strategy. In the past, you would have had great performance beside a drawdown, that was from psychological point of view good to handle with. This strategy additionally uses STOP LOSSES, so you can never loose you whole ordersize at one trade.
  
  
• Slippage:
  You also must consider about getting slipped when trading this strategy on live markets. Statistically one could assume, that the slippage could be neutral, as it can be both positive or negative. It depends on your execution time, the exchange, on which you are executing trades and market conditions. But keep it in mind, as if you have too much slippage, this strategy would be unprofitable.
  

Catatan Rilis:

v1.7.7: Improved overall backtesting performance by addign additional MACD condition to take profit algorithm

Catatan Rilis:

Improved Performance by additional trend reversal conditions, seen as "ATR SL (Long|Short)"