fetches asset data - pandas

calculate indicators

TA = vbt.IndicatorFactory.from_pandas_ta(indicator)  
TA.run(**dict_indicators)

loop over strategies

run stategy

vbt.Portfolio.from_signals(
    asset data close, 
    asset data entries, 
    asset data exit, 
    cash)

fetches asset data

in/out samples (rolling)

simulate holding -> sharpe ratio

in-sharpe (simulate all params) best_index (in-sharpe) performance[performance.groupby('split_idx').idxmax()].index best_params -> fast, and slow: vbt.MA.run entries = fast_ma.ma_above(slow_ma) #, crossover=True) exits = fast_ma.ma_below(slow_ma) #, crossover=True) pf = vbt.Portfolio.from_signals sharpe_ratio

out-sharpe (simulate all params) best_index (out-sharpe)

This wrapper is an enhancement of the Python library backtesting.py, which is very popular for backtesting of trading strategies.

Will extract this as a seperate repo, and deploy to PyPi as a library

Issues addressed:

• backtesting.py relies on hardcoding of strategy configuration, such as upper and lower bands, and also the optimisation criteria, etc., meaning much code has to be written to define strategies.
• The default strategies, and many circulated online, are not going to find alpha (an opportunity to exploit market inefficiency); because they are utilised by too many traders.

Solution:

• The wrapper assembles strategy details according to content of either a config file, or a database. (WIP: This can then be edited via a UI.)
• There is a factory to fetch the strategy class.
• Walkforward testing of all strategies.
• Strategies, with optimisation functionality, are required that compare data sources of world events, including news events and social media sentiment, to market movements to find patterns that reflect correlation. market_sentiment_ms
• Optimsiation can be ehanced with machine learning stonk-value-forecasting-model

Image	Details
	strategies folder: Typical technical indicators, such as Average Direction Movemement, Bollinger Bands, Donchian, EMA, MAC, MACD, Mean Reversion, Momentum, RSI, SMA, Stochastic, etc., and combinations of these.

Sample stategy

• 10-day SMA below 30-day SMA.
• 10-day and 30-day SMA above 50-day SMA.
• 10-day, 30-day, and 50-day SMA below 200-day SMA.

Purpose: Entry/exit points; trend confirmation; and risk management.

The MACD indicator is derived from two exponential moving averages (EMAs) — the 12-day EMA and the 26-day EMA. The formula for MACD is as follows:

MACDLine=12−dayEMA−26−dayEMA

A signal line, often a 9-day EMA, is plotted on top of the MACD line. This signal line serves as a trigger for buy or sell signals.

SignalLine=9−dayEMA

The MACD histogram, the visual representation of the difference between the MACD line and the signal line, provides insights into the strength and direction of the trend.

MACDHistogram=MACDLine−SignalLine

2 conditions:

• MACD above the MACD signal.
• MACD greater than 0.