Chad-Thackray
The video demonstrates various techniques for visualizing trades using Plotly in Python. The presenter shows how to create a candlestick chart, scatter plot, and RSI indicator chart while highlighting trade entry and exit points. They also show how to customize the visualizations by adjusting colors and markers and how to add additional information to the hover data. The video emphasizes the importance of plotting entry and exit signals at the appropriate times rather than where they were executed to avoid confusion and ensure accurate backtesting. Overall, the techniques demonstrated can be used to create custom dashboards without relying on pre-built ones in a backtesting framework.
In this section of the video, the speaker explains how to visualize trades in Python using Plotly. They demonstrate a visualization that shows the entry and exit points of a trade, the time and price, the trade's size, and the overall percentage profit. They also show a subplot displaying an indicator with the entry and exit points. Both the main and the subplot share an x-axis, making it easy to align them and see what's going on with the trades. The speaker then goes on to explain how to create any indicator we want to plot, generate some trades using a basic backtesting framework, and manipulate the data required for the visualization.
In this section, the speaker discusses how to create a basic Candlestick chart using Plotly and how to customize it to highlight trade entries and exits. They start by plotting the open high low close candles using a preset in Plotly and then proceed to adjust the colors and opacity of the candles. Next, they show how to add entry signals to the chart by adding a scatter plot with markers representing each entry signal. The speaker also discusses how to customize the hover data to include additional information such as the entry price and time, trade size, exit price and time, and profit percentage.
In this section, the speaker demonstrates how to visualize trade entries using Plotly. By plotting the X-axis as the entry time and the Y-axis as the entry price, the resulting graph shows each trade as a diamond marker point. To customize the style of the marker, the speaker changes the mode to "markers" and sets the marker symbol to a diamond dash dot shape. Additionally, the speaker changes the size and color of the marker to make it stand out and differentiates it from exit markers, which will be added later. The speaker also demonstrates how to modify the hover template to display more meaningful information, in this case, the entry price of each trade.
In this section, the video demonstrates how to customize the data shown in the scatter plot through the use of custom data parameter. Indexing into the custom data can display various values such as trade size, entry time, and profit percentage by simply pasting one line of code and adjusting the index number. The video also shows how to format the numbers to a specific decimal place. The use of name parameter makes it easier to label the entries and exits in the legend. Lastly, adding exits is easily done by copying and pasting the entry code and changing the colors and label accordingly.
can adjust it by setting the vertical spacing between the two plots using the `vertical_spacing` variable. In this section of the video, the presenter demonstrates how to add the RSI indicator to the graph and add entry and exit signals onto the indicator to check that everything is working properly. They also show how to link the Open high low close graph and the indicator together by setting the shared x-axis to true and adjust the size and spacing of the plots to make it easier to read and analyze. All of this is done using the plotly library without relying on a backtesting framework.
In this section of the video, the speaker demonstrates how to add entry and exit signals onto a plot of the RSI curve to make it easier to read and check execution logic. The speaker adjusts the vertical spacing of the plot to allow for more space and labels the entry and exit signals with different markers and colors. The entry signals are labeled with RSI values at the time of trade execution and the exit signals are labeled with the corresponding exit times. The speaker notes the importance of plotting the signals at the time they were generated rather than executed and shows how to shift the times using timedelta.
In this section, the speaker discusses how to visualize trade entries and exits using Plotly. They demonstrate how to plot the signals generated by trades at the appropriate times, rather than where they were executed, to avoid confusion. By using this technique, traders can identify small errors in order execution and ensure that their backtests accurately simulate their strategy. The skills learned in this video can be used to create custom dashboards rather than relying on pre-built ones in any backtesting framework.
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