prototyping trading strategies in python

The fastest path todannbsp;test the gainfulness of a trading model generating signals is to get along a simple backtest (which substance no hindsight biases i.e at least 1 period of timeframe lag from signal even if you timeframe is in milliseconds) using historical time series.

actual returns = absolute return (no hindsight biases to signal) – transaction cost – spillage

Spillage really matters when the trade exemplar is tested on live markets. Examples are when the spread widens connected first appearance such that without the median price moving, the opposite bid operating theater ask (if long or squabby) hits the occlusion loss.

Toolbox:

Information from Quandl – free registration and bu use their API token in pythonhttps://www.quandl.com/

pandas – good for munging (merging and creating parvenu derived dannbsp;timeseries) and plotting clip series, where support of datetime goes to milliseconds

seaborn – for more advanced plots much as conditional regression besides as segmented correlation or scattered plots

numpy – mention here since its very righteous for exact expressions with its vectorisation capabilities. It performs fast because numpy is coded in C and Fortan.

Work done in Spyder, a Python interface

Plot of key macro variables where prices changes signal a convert in the profitable demand and supply

Germ: Quandl, Python

Source: Quandl, Python

Conditional retroversion subplots with seaborn

Noticeable changes in slope of regression (sensitivity) when conditioned on VIX being in a higher place 90%dannbsp;centile (classified as "fear" regime), the other way around. Returns rather than level values are used i.e. Incline is row Beta against column arsenic determinant. Weakness here is that outliers presence causes the slope to be inclined widely for some subplots.

Common trends…

SPX and VIX are actually negative correlated, but less so if market is already in a fear

When market is fearful…

DXY and near yield becomes less positively correlated
SPX and near yield raises more to a rise in crude oil price (signs of ostentation)
SPX raises more to a fall in ungenerous bond prices (equities-bond rotation)
gold becomes a safe heaven or "a hedging tool todannbsp;equities bear" and switches to direct correlation with fear index
DXY fall and rise becomes more restrained as risk off sentiment slows down theoretic working capital flow

Source: Quandl, Python

Backtest Results
#=========================================================================
# strategy 1 – long SPX when VIX at a lower place 75% percentile of a long chronicle
#=========================================================================

Strategy performs -105.351% over buy-and-holdIn

#=========================================================================
# strategy 2 – long SPX when USSG2YR preceding 3 months simple mediocre
#=========================================================================

Strategy performs -143.764% terminated buy-and-clutches

#=========================================================================
# strategy 3 – long DXY when USSG2YR above 1 month simple average
#=========================================================================

Scheme performs -66.034% over grease one's palms-and-hold in

#=========================================================================
# scheme 4 – long SPX when XAU below 1 calendar month simple average
#=========================================================================

Strategy performs -48.412% over buy-and-hold