[BTSIM Synth .CSV Outputs, ***PRE’s ((pre-created//pre-generated)) algo(rithmic) key(s); is it tracking to forward exp. bt. sim. (?) ](https://triangular-cough-c11.notion.site/199828ff6bf7809b887fc1997f149910)
‘SYMBRES’ AKA profit per symbol or $PPS (’cumulative_residuals’ per symb.);
2-3 sharpe synthetic .CSV base method PRE’s*** ((pre-created//pre-generated)) ‘cumulative_returns’, ‘cumulative_residuals’
[BASE_SYMBOL_X_PAIRS][DATAFRAME_ATTRIBUTES]__.pdf
((scatter//pareto)) in-built .CSV tools; base_method(s)_v0.
1m_aggs_temp_with_residuals_scatter (2).csv
1m_aggs_temp_with_returns_pareto_cumulative_returns (1).csv
‘cumulative_residuals’, ‘cumulative_returns’ base method v0 .csv; NEW ((scatter//pareto)) peak prominent symbs. graph functionality via in-built .CSV tools for easy allocator use (Top 1% Peak Prominent Symbols)!
‘cumulative_returns’, ‘cumulative_residuals’ algo(rithmic) keys as .plot() -ing; ‘cumulative_returns’ or ‘CUMRETS’ or ‘SYMBRES’ ((here)) graph functionality.
##### PLOT CUMULATIVE RESIDUALS PER SYMBOL:
# Create correlation plot
plt.figure(figsize=(15, 12))
plt.subplot(3, 1, 1)
for symbol in df['Symbol'].unique():
symbol_data = df[df['Symbol'] == symbol]
plt.plot(
symbol_data['Timestamp'],
symbol_data['cumulative_residuals'],
label=f'{symbol} Cumulative Residuals'
)
plt.title('Statistical Arbitrage Cumulative Residuals')
plt.xlabel('Time')
plt.ylabel('Cumulative Returns')
plt.legend()
plt.grid(True)
Take 500 stks., 3.67 Yrs. ((port//version)) <> 3000 stks., 5 Yrs., 7500 stks. (…). Agn. names (accum. baskets), duration (btsim synth .csv up to 100+ yrs. forward-backward, algos multi-key ***PRE’s columns).
How to graph single columnar cross-sectional ‘SYMBRES’; 3.67 Yrs., 500 stks.; rolling dist alpha params.