Our latest update to the platform now gives you the power to visually analyze all your closed positions and trading results. This means you can dive deep into the essential factors driving your performance history and make intelligent decisions based on your trading statistics and data.
The new ‘Analyze’ tab lets you evaluate key strategy metrics and position averages on one comprehensive page. You can quickly reference different variables, such as the ticker symbol and position type, to determine what has been successful in your portfolio.
Easily toggle between live and paper trading bots and sort by symbol, tags, strategies, accounts, and bots. All the information updates instantly to display the filtered stats.
Focus on specific strategies to see the performance of different position types:
Examine how different symbols have performed:
You can even click on a symbol or strategy type to see a complete list of position details for that pre-filtered list, helping you gain even more insights:
Plus, you can do all this for your entire portfolio or focus on a single trading account across different lookback periods. Use custom date ranges and select multiple bots for detailed performance history.
Why analyzing portfolio performance is important
As traders, we need access to our strategy’s historical performance to inform our decisions with objective data. Analyzing our trades allows us to investigate the root cause of a strategy’s success or failure, determine what position types do best in different market environments, uncover how certain tickers are impacted across strategies, or see if a few large losses wipe out many wins, just to name a few.
For example, you can focus on specific symbols when evaluating performance. Maybe some tickers outperform others? By filtering for some symbols and not others you can quickly slice and dice performance to see what groups of symbols worked, and which didn’t.
The power of Tags
The power of tagging positions is truly revealed with the new analyzer and may help you discover critical ‘aha’ moments after tagging positions based on the bot’s statefulness or market conditions.
For example, I’m able to evaluate how positions entered in a market condition I tagged ‘Strong Uptrend’ performed year-to-date:
Not so great, especially for bullish short put spreads, which we might think should've been the perfect strategy for a strong uptrend, right? Conversely, positions tagged and entered in a ‘weak uptrend’ or ‘uptrend’ performed well.
Without tags, I would have never been able to gain this type of clarity. Now that we can analyze our trading results, I can use this insight to optimize the bot's performance going forward.