It builds upon the idea that each instance of a trading strategy can be represented in a vectorized form, so multiple strategy instances can be packed into a single multi-dimensional array. And since there were no packages in the Python ecosystem that could even closely match the speed of my own backtests, I made vectorbt.Vectorbt combines pandas, NumPy and Numba sauce to obtain orders-of-magnitude speedup over other libraries. Lessons with a break of 10 minutes, ecologist, each faculty has its own building, to apply to, to arrange the courses, the lecture, subjects, two terms a.After trying tweaking pandas, multiprocessing, and even evaluating my strategies on a cluster with Spark, I finally found myself using Numba - a Python library that can compile slow Python code to be run at native machine code speed. Actions intended to be performed in I am going to visit my parents on next week, the near. This not only prolongs your lifecycle of designing strategies, but is dangerous after all: limited number of tests is similar to a tunnel vision - it prevents you from seeing the bigger picture and makes you dive into the market blindly.Present Continuous this week fixed arrangements (plans) in the near She is seeing her dentist this week at the weekend future (definite time) on Monday Present Simple at 10.45 timetables The train leaves at 3 o’clock at 5 am Going to in one year, 1. Questions like "Which strategy is better: X or Y?" require fast computation and transformation of data.Build interactive charts/dashboards without leaving JupyterThe current implementation has limitations though: Test thousands of strategies, configurations, assets, and time ranges in one go Supercharge pandas and your favorite tools to run much faster Analyze and engineer features for any time series data You can find basic examples and explanations in the documentation.Below is an example of doing in total 67,032 tests on three different timeframes of Bitcoin price history to explore how performance of a MACD strategy depends upon various combinations of fast, slow and signal windows:Import vectorbt as vbt import numpy as np import yfinance as yf from itertools import combinations, product # Fetch daily price of Bitcoin price = yf.Ticker("BTC-USD").history(period="max") price = price.vbt.split_into_ranges(n=3) # Define hyper-parameter space # 49 fast x 49 slow x 19 signal fast_windows, slow_windows, signal_windows = vbt.indicators.create_param_combs( (product, (combinations, np.arange(2, 51, 1), 2), np.arange(2, 21, 1))) # Run MACD indicator macd_ind = vbt.MACD.from_params( price, fast_window=fast_windows, slow_window=slow_windows, signal_window=signal_windows, hide_params= ) # Long when MACD is above zero AND signal entries = macd_ind.macd_above(0) & macd_ind.macd_above(macd_ind.signal) # Short when MACD is below zero OR signal exits = macd_ind.macd_below(0) | macd_ind.macd_below(macd_ind.signal) # Build portfolio portfolio = vbt.Portfolio.from_signals( price.vbt.tile(len(fast_windows)), entries, exits, fees=0.001, freq='1D') # Draw all window combinations as a 3D volume fig = portfolio.total_return.vbt.volume( x_level='macd_fast_window', y_level='macd_slow_window', z_level='macd_signal_window', slider_level='range_start', template='plotly_dark', trace_kwargs=dict( colorscale='Viridis', colorbar=dict( title='Total return', tickformat='%' ) ) ) fig.show() From signal generation to data visualization, the example above needs roughly a minute to run. It also integrates Plotly and ipywidgets to display complex charts and dashboards akin to Tableau right in the Jupyter notebook.Usage requires intermediate knowledge of pandas and NumPy to understand what's going on. The issue can be mitigated by deleting at least some artifacts as soon as they are created and by disabling caching. Above example created multiple DataFrames each taking 46MB of RAM (price, signals, cash, shares, equity, returns, etc). Fast processing means more memory requirements.Cheers.Submitted by plkwo to algotrading $SPCB SuperCom main roundup(Global Leader in Digital Solutions For COVID-19)SuperCom mobilizes resources and teams in attempts to support URGENT requests from governments to help restrict spread of the (COVID-19). It's more of a data mining tool to get to know your market and approach better.If it sounds cool enough, try it out! I would love if you'd give me some feedback and contribute to it at some point, as the codebase has grown very fast. Finally, if you're looking for a pure backtesting solution - it's not. The approach of merging vectorized and iterative code differs significantly from classic OOP approach of designing strategies, and will require you to rethink how strategies are formulated and implemented (which is kinda fun). I tried to make lots of small examples in the documentation to get the idea how everything is glued together.
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