Spider — Indicators and Strategies

Financial Radar Chart by zdmre Radar chart is often used when you want to display data across several unique dimensions. Although there are exceptions, these dimensions are usually quantitative, and typically range from zero to a maximum value. Each dimension’s range is normalized to one another, so that when we draw our spider chart, the length of a line from zero to a dimension’s maximum value will be the similar for every dimension. This Charts are useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance. How is the score formed? Debt Paying Ability if Debt_to_Equity < %10 : 100 elif < 20% : 90 elif < 30% : 80 elif < 40% : 70 elif < 50% : 60 elif < 60% : 50 elif < 70% : 40 elif < 80% : 30 elif < 90% : 20 elif < 100% : 10 else: 0 ROIC if Return_on_Invested_Capital > %50 : 100 elif > 40% : 90 elif > 30% : 80 elif > 20% : 70 elif > 10% : 50 elif > 5% : 20 else: 0 ROE if Return_on_Equity > %50 : 100 elif > 40% : 90 elif > 30% : 80 elif > 20% : 70 elif > 10% : 50 elif > 5% : 20 else: 0 Operating Ability if Operating_Margin > %50 : 100 elif > 30% : 90 elif > 20% : 80 elif > 15% : 60 elif > 10% : 40 elif > 0 : 20 else: 0 EV/EBITDA if Enterprise_Value_to_EBITDA < 3 : 100 elif < 5 : 80 elif < 7 : 70 elif < 8 : 60 elif < 10 : 40 elif < 12 : 20 else: 0 FREE CASH Ability if Price_to_Free_Cash_Flow < 5 : 100 elif < 7 : 90 elif < 10 : 80 elif < 16 : 60 elif < 18 : 50 elif < 20 : 40 elif < 22 : 30 elif < 30 : 20 elif < 40 : 15 elif < 50 : 10 elif < 60 : 5 else: 0 GROWTH Ability if Revenue_One_Year_Growth > %20 : 100 elif > 16% : 90 elif > 14% : 80 elif > 12% : 70 elif > 10% : 50 elif > 7% : 40 elif > 4% : 30 elif > 2% : 20 elif > 0 : 10 else: 0

Multi Time Frame Normalized Price Enhance Your Trading Experience with the Multi Time Frame Normalized Price Indicator Introduction As a trader, having a clear and informative chart is crucial for making informed decisions. In this post, we will introduce the Multi Time Frame Normalized Price (MTFNP) Indicator, an innovative trading tool that offers an insightful perspective on price action. The script creates a symmetric chart, with the time axis going from top to bottom, making it easier to identify potential tops and bottoms in various ranges. Let's dive deeper into this powerful tool to understand how it works and how it can improve your trading experience. The Multi Time Frame Normalized Price Indicator The MTFNP Indicator is designed to provide a comprehensive view of price action across multiple time frames. By plotting the normalized price levels for each time frame, traders can easily identify areas of support and resistance, as well as potential tops and bottoms in various ranges. One of the key features of this indicator is the symmetry of the chart. Instead of the traditional horizontal time axis, the MTFNP Indicator plots the time axis vertically from top to bottom. This innovative approach makes it easier for traders to visualize the price action across different time frames, enabling them to make more informed decisions. Benefits of a Symmetric Chart There are several advantages to using a symmetric chart with a vertical time axis, such as: Easier to read: The unique layout of the chart makes it easier to analyze price action across multiple time frames. The clear separation between each time frame helps traders avoid confusion and identify important price levels more effectively. Identifying tops and bottoms: The symmetric presentation of price action enables traders to quickly spot potential tops and bottoms in various ranges. This can be particularly useful for identifying potential reversal points or areas of support and resistance. Improved decision-making: By offering a comprehensive view of price action, the MTFNP Indicator helps traders make better-informed decisions. This can lead to improved trading strategies and ultimately, better results. The MTFNP Indicator Script The MTFNP Indicator script leverages several custom functions, including the Chebyshev Type I Moving Average, to provide a smooth and responsive signal. Additionally, the indicator uses the Spider Plot function to create a symmetric chart with the time axis going from top to bottom. To customize the MTFNP Indicator to your preferences, you can adjust the input parameters, such as the standard deviation length, multiplier, axes color, bottom color, and top color. You can also change the scale to fit your desired chart size. Exploring the Relationship between Min, Max Values and Time Frames In the Multi Time Frame Normalized Price (MTFNP) script, it is crucial to understand the relationship between the min and max values across different time frames. By analyzing how these values relate to each other, traders can make more informed decisions about market trends and potential reversals. In this section, we will dive deep into the relationship between the current time frame's min and max values and those of the further-out time frames. Interpreting Min and Max Values Across Time Frames When analyzing the min and max values of the current time frame in relation to the further-out time frames, it is essential to keep in mind the following points: All min values: If the current time frame and all further-out time frames have min values, this is a strong indication that the current price level is not just a local minimum. Instead, it is likely a more significant support level. In such cases, there is a higher probability that the price will bounce back upwards, making it a potentially favorable entry point for a long position. All max values: Conversely, if the current time frame and all further-out time frames have max values, this suggests that the current price level is not just a local maximum. Instead, it is likely a more significant resistance level. In these situations, there is a higher probability that the price will reverse downwards, making it a potentially favorable entry point for a short position. Neutral values with high current time frame: If the current time frame has a high value while the further-out time frames are more neutral, it could indicate that the trend may continue. This is because the high value in the current time frame may signify momentum in the market, whereas the neutral values in the further-out time frames suggest that the trend has not yet reached an extreme level. In this case, traders might consider following the trend and entering a position in the direction of the current movement. Neutral values with low current time frame: If the current time frame has a low value while the further-out time frames are more neutral, it could indicate that the trend may reverse. This is because the low value in the current time frame may suggest a potential reversal point, whereas the neutral values in the further-out time frames imply that the trend has not yet reached an extreme level. In this case, traders might consider entering a counter-trend position, anticipating a potential reversal. Balancing Different Time Frames for Optimal Decision Making It is essential to remember that relying solely on min and max values across different time frames can lead to potential pitfalls. The market is influenced by a wide array of factors, and no single indicator or data point can provide a complete picture. To make the most informed decisions, traders should consider incorporating additional technical analysis tools and evaluating the overall market context. Moreover, it is crucial to maintain a balance between the current time frame and the further-out time frames. While the current time frame provides information about the most recent market movements, the further-out time frames offer a broader perspective on the market's historical behavior. By combining insights from both types of time frames, traders can make more comprehensive assessments of potential opportunities and risks. Conclusion In conclusion, the Multi Time Frame Normalized Price (MTFNP) script offers traders valuable insights by analyzing the relationship between the current time frame and further-out time frames. By identifying potential trend reversals and continuations, traders can make better-informed decisions about market entry and exit points. Understanding the relationship between min and max values across different time frames is an essential component of using the MTFNP script effectively. By carefully analyzing these relationships and incorporating additional technical analysis tools, traders can improve their decision-making process and enhance their overall trading strategy. However, it is important to remember that relying solely on the MTFNP script or any single indicator can lead to potential pitfalls. The market is influenced by a wide array of factors, and no single indicator or data point can provide a complete picture. To make the most informed decisions, traders should consider using a combination of technical analysis tools, evaluating the overall market context, and maintaining a balance between the current time frame and the further-out time frames for a comprehensive understanding of the market's behavior. By doing so, they can increase their chances of success in the ever-changing and complex world of trading.

Spider_Plot Introduction: Spider charts, also known as radar charts or web charts, are a powerful data visualization tool that can display multiple variables in a circular format. They are particularly useful when you want to compare different data sets or evaluate the performance of a single data set across multiple dimensions. In this blog post, we will dive into the world of spider charts, explore their benefits, and demonstrate how you can create your own spider chart using the Spider_Plot library. Why Spider Charts are Cool: Spider charts have a unique visual appeal that sets them apart from other chart types. They allow you to display complex data in a compact, easy-to-understand format, making them perfect for situations where you need to convey a lot of information in a limited space. Some of the key benefits of spider charts include: Multi-dimensional analysis: Spider charts can display multiple variables at once, making them ideal for analyzing relationships between different data sets or examining a single data set across multiple dimensions. Easy comparison: By displaying data in a circular format, spider charts make it simple to compare different data points, identify trends, and spot potential issues. Versatility: Spider charts can be used for a wide range of applications, from business and finance to sports and health. They are particularly useful for situations where you need to analyze performance or make comparisons between different entities. Creating Your Own Spider Chart with the Spider_Plot Library: The Spider_Plot library is a user-friendly, easy-to-use tool that allows you to create stunning spider charts with minimal effort. To get started, you'll need to import the Spider_Plot library: import peacefulLizard50262/Spider_Plot/1 With the library imported, you can now create your own spider chart. The first step is to normalize your data. Normalizing ensures that all data points fall within the 0 to 1 range, which is important for creating a visually balanced spider chart. The Spider_Plot library provides the data_normalize function to help you normalize your data. This function accepts several parameters, including the normalization style ("All Time", "Range", or "Custom"), length of the range, outlier level, lookback period for standard deviation, and minimum and maximum values for the "Custom" normalization style. Once you have normalized your data, you can create an array of your data points using the array.from function. This array will be used as input for the draw_spider_plot function, which is responsible for drawing the spider plot on your chart. The draw_spider_plot function accepts an array of float values (the normalized data points), an array of background colors for each sector, a color for the axes, and a scaling factor. Example Usage: Here's an example script that demonstrates how to create a spider chart using the Spider_Plot library: oc = data_normalize(ta.ema(math.abs(open - close), 20), "Range", 20) // Create an array of your data points data = array.from(tr, rsi, stoch, dev, tr, oc, tr) // Define colors for each sector colors = array.from(color.new(color.red, 90), color.new(color.blue, 90), color.new(color.green, 90), color.new(color.orange, 90), color.new(color.purple, 90), color.new(color.purple, 90), color.new(color.purple, 90)) // Draw the spider plot on your chart draw_spider_plot(data, colors, color.gray, 100) In this example, we have first normalized six different data points (rsi, source, stoch, dev, tr, and oc) using the data_normalize function. Next, we create an array of these normalized data points and define an array of colors for each sector of the spider chart. Finally, we call the draw_spider_plot function to draw the spider chart on our chart. Conclusion: Spider charts are a versatile and visually appealing tool for analyzing and comparing multi-dimensional data. With the Spider_Plot library, you can easily create your own spider charts and unlock valuable insights from your data. Just remember to normalize your data and create an array of data points before calling the draw_spider_plot function. Happy charting! Library "Spider_Plot" data_normalize(data, style, length, outlier_level, dev_lookback, min, max) data_normalize(data, string style, int length, float outlier_level, simple int dev_lookback, float min, float max) Parameters: data (float) : float , A float value to normalize. style (string) : string , The normalization style: "All Time", "Range", or "Custom". length (int) : int , The length of the range for "Range" normalization style. outlier_level (float) : float , The outlier level to exclude from calculations. dev_lookback (simple int) : int , The lookback period for calculating the standard deviation. min (float) : float , The minimum value for the "Custom" normalization style. max (float) : float , The maximum value for the "Custom" normalization style. Returns: array , The normalized float value. draw_spider_plot(values, bg_colors, axes_color, scale) draw_spider_plot(array values, array bg_colors, color axes_color, float scale) Parameters: values (float ) : array , An array of float values to plot in the spider plot. bg_colors (color ) : array , An array of background colors for each sector in the spider plot. axes_color (color) : color , The color of the axes in the spider plot. Default: color.gray scale (float) : float , A scaling factor for the spider plot. Default: 10 Returns: void , Draws the spider plot on the chart.