Quansium Series A Backtest

17 Ara 2020

This comes with preconfigured setups or strategies. Simply choose one from our list based on the timeframe it was made for. Leverage can be changed; to keep trading safe, a maximum of 2 is allowed. In our findings, this was able to trade crypto (specifically BTC ), MES (Micro E-mini S&P 500 Index Futures ), and stocks. It is important to know that setups A, B, C, and D use variable position sizing, and dynamic stop loss/trailing stop/take profit, these parameters are provided through the alerts. The rest of the strategies were created with a simpler approach in mind, just plainly entry/exits signals.

Quansium as a framework:

• Price reformat: we take the price source (Open, Close, High, Low) and remove any noise that affects the accuracy of our signals.
  
• Time awareness: we take several time periods of the data on the chart such as start, end, and whole. We use this to add more depth to our signals.
  
• Position size: our backtest tries to recreate as much as the real world trades as possible so our position is determined by the current equity. We also use the volatility of the market to increase or decrease our exposure or risk.
  
• Risk awareness: stop loss, take profit, trailing stop are the risk exits we use to provide our users some peace of mind. These parameters are totally dynamic and follow the same behavior of the market.
  
• Signals filtering: to make almost non-existent any errors and increase the quality of our trades, our indicators go through multiple phases, this avoid double entries or early exits, and help maintain a record of what has transpired and what’s currently taking place.
  
• Indicators: whenever we can we use custom code or our own functions instead of the defaults ones provided. This gives us total control of what we’re trying to achieve. In many cases we tend to combine several indicators’ logic into one creating a more personalized take on it.
  
• Easiness: since we started our main goal has been to provide the easiest and fastest way to alerts’ creation. It has taken us years to reach this level where now we already provide a list of preset strategies so the user doesn’t have to spend much time tinkering with scripts and more on other matters, because we know life is more than just trading.
  
• Raw signals: we provide the option to turn off as much of our advanced features such as stop loss, take profit, trailing stop, dynamic sizing, etc, etc for a simple approach. Trade signals still go through the signals filtering method mentioned above,
  
• Timeframe pairing: we take trading very seriously, by no way we’ll want the user to lose money (although such thing is expected because past results aren’t an indicative of futures ones), through years of experience we have found what are usually common mistakes the user makes, this feature allows us to only activate the strategy if the right timeframe is chosen.
  
• Trend filters: through the years we have improved the arts of the trend. We like to keep things simple but yet powerful. We observe the macro and micro trend of the security. This helps confirm we are entering at the desirable timing. We also incorporate volume and volatility into decision making, we simply programmed it to trade when these are increasing and higher than the average values observed in both the short and long term. Finally we take into account the strength of the pair to make our final choice of whether to enter or wait, and if anything flashes contrary movement then we cancel the upcoming signal and stop monitoring until the next one comes along.
  
• Full automated risk: stop loss, take profit, and trailing stops usually are set in percentages, and optimized even more using the current market behavior to become more adaptive. But always remains some sort of fixation, so the user must choose a value somewhere. This is where our framework shines the most, as previously mentioned before when we take time into our calculations, we use several periods to observe performance and get values that keep our risk exits natural and closest to the flow of the market itself.
  

Setups:

• A: Centered oscillator with the difference of several moving averages with more sensitive settings. Momentum focused.
  
• B: Centered oscillator using simple moving averages. Trend-Following focused.
  
• C: Centered oscillator using smoothed data with the help of faster moving averages. Trend-Following focused.
  
• D: Centered oscillator with the difference of several moving averages with less sensitive settings. Trend-Following focused.
  
• E: Centered oscillator with the difference of moving averages where the standard deviation is applied first. It uses less sensitive settings. Trend-Following focused.
  
• F: Finds the relationship between multiple readings of the price’s relative strength to better pin-point downs and ups. Trend-Following focused.
  
• G: Centered oscillator with the difference of moving averages where the standard deviation is applied first. It uses more sensitive settings. Momentum focused.
  
• H: Multiple centered oscillators using various moving averages. Trend-Following focused.
  
• I: Centered oscillator using simple moving averages. Momentum focused.
  

Note: The framework is composed of almost 1000 lines of code as compared to each indicator that makes up the setup which is around 10. The power from Quansium doesn't come from the strategies themselves but rather the overall system that turns simple signals into complex and advanced trades.

Strategy Tester:

• Initial Capital: chosen value is $20,000, as an approximate to Bitcoin’s ATH (All-Time High). In previous iterations we noticed some trades won’t go through if the capital was less than the ATH.
  
• Order Size: 100% of equity (although the script controls this, and this is of no regards to the results).
  
• Pyramiding: 1, system doesn’t place multiple entries in a row, only one at a time.
  
• Commission: This simulates order execution with custom trading fees. Commissions are turned off by default because this script works in various markets and each operates differently. In order to reach results that are close to real world conditions, it is imperative the user fills this based upon their broker or exchange data.
  

When we started, we were focused on finding the best indicator, or creating it ourselves. After years we came to realize that the secret is not in which indicator you use but the framework behind it. All strategies have bad, good, best, worst performance periods. The key of a good system is to help keep you safe when it’s down and maximize your potential when it’s up. We hope this material at the very minimum inspires you to keep going and not lose faith, because it is not the smartest who win but those who persevere.