Python
slippage correlation volume deep dive - data analysis
slippage been great november.
wanted to understand why.
data analysis time.
november slippage performance # november avg (through nov 18): 1.9 ticks
adaptive position sizing - regime-based approach
·1261 words
position sizing makes or breaks algo trading.
been refining adaptive approach last 6 months.
finally working consistently.
the problem with static sizing # most algo traders:
regime detection - walk-forward validation improving accuracy
·806 words
regime detection upgraded.
walk-forward validation running.
accuracy improving.
the problem # static regime parameters:
optimized on historical data.
regime detection filtering framework - how i adapt to market conditions
·1334 words
august forcing me to rely on filters.
figured worth explaining how regime detection works. learned a lot from options selling regime discussions on NexusFi about adapting to conditions.
the problem # strategies don’t work in all conditions.
walk-forward optimization - how i avoid overfitting my strategies
overfitting = #1 way algos fail in production.
backtest looks amazing.
live trading implodes.
walk-forward optimization prevents this.
been discussing validation techniques on NexusFi algo trading threads and walk-forward is the gold standard.
chicago colocation - 67ms to 12ms latency improvement, worth the cost
·956 words
moved execution server to chicago colo march 2024.
3 months data in.
latency dropped 67ms → 12ms average.
why chicago # CME exchange location: chicago
regime detection improvements - faster market adaptation working
·1101 words
week 3 april going strong.
adaptive strategy crushing it.
been refining regime detection logic.
current performance (apr 1-17) # trades: 29
strategy overhaul - adapting algos to new market regime
·1264 words
february crushed my strategies.
mean reversion dropped from 81% to 57% win rate.
market regime changed.
strategies need to adapt.
been discussing regime adaptation on r/algotrading. other algo traders dealing with same shit.
polygon.io vs alpha vantage - which data feed for algo trading
·1165 words
data feeds = foundation of algo trading.
garbage data = garbage trades.
i’ve used both polygon.io and alpha vantage extensively.
spent months researching data feeds when i started trading. NexusFi community helped narrow down options to these two.
backtesting overfitting - how i avoid curve-fitting my algos
·1376 words
backtesting is where most algo traders hurt themselves.
they optimize parameters until strategy looks perfect on historical data.
then go live and it fails immediately.
classic overfitting.
learned this the hard way. saw countless traders on NexusFi backtesting discussions make same mistake when i joined in 2023.