LPPL Indicator Backtest on XRPUSD Daily

Log-Periodic-Power-law - Colab notebook for both non-coders and coders to add LPPL signals to any desired price data using the Python LPPLS library.
Remember this calculation is very heavy and can take some time to run. It is meant for larger time frames and bubbles where herd buying or selling is taking place. Am personally not a fan of the backtesting used in this video, but I know others are looking to backtest due to popularity so wanted to provide an easy path to do this.
Previous video:
• Identify Crypto Market...
Colab notebook: colab.research.google.com/dri...
Website: cryptowizards.net

[@moondevonyt]

mad props for setting up that google collab notebook for those who aren't code savvy though, gotta say, relying solely on backtesting for predicting black swan events feels a bit iffy since markets are often driven by unpredictable factors but respect for making crypto analysis accessible for the masses keep schooling us wizard!

[@vladk9152]

By fitting the entire dataframe to a curve and then using the results for a backtest you introduced look forward bias.

[@helex050]

Example and backtest on stocks would be nice 😅

[@morabergel5063]

Thank you for another inspiring video!! By the way, how is it going with "Flash Loan Arbitrage for No Code Trading"? We are all waiting and expecting for part 2 :)

[@CryptoWizards]

The challenge right now is the efficiency and cost of managing the full node on Binance smart chain and getting the cross exchange smart contract sorted. I’ll know definitively by the start of next week whether we are green or red on the project.

[@mervemert3805]

Hello, I am trying to analyze with colab using the data set you analyzed in the video. But after running the code on line 8, I encounter the error as stated below. When I check, my csv file is the same. Can you help me with why I am getting this error even though I use the same data and the same dates? return (w / (2.0 * np.pi)) * np.log((tc - t1) / (tc - t2)) /usr/local/lib/python3.10/dist-packages/lppls/lppls.py:617: RuntimeWarning: invalid value encountered in log return (w / (2.0 * np.pi)) * np.log((tc - t1) / (tc - t2)) /usr/local/lib/python3.10/dist-packages/lppls/lppls.py:617: RuntimeWarning: invalid value encountered in log return (w / (2.0 * np.pi)) * np.log((tc - t1) / (tc - t2))