DOGE Reddit Mentions | ChartExchange

I just published a full write-up on a multi-asset crypto portfolio strategy built around an adaptive Kalman filter in Backtrader.
Medium link:
[https://pyquantlab.medium.com/adaptive-kalman-filter-crypto-portfolio-with-backtrader-ccxt-and-quantstats-44bff5e2b3f9](https://pyquantlab.medium.com/adaptive-kalman-filter-crypto-portfolio-with-backtrader-ccxt-and-quantstats-44bff5e2b3f9?utm_source=chatgpt.com)
High-level idea
Instead of using a classic trend indicator (EMA, MACD, etc.), I model price as a 2D state:
* x\_t = \[price, velocity\]ᵀ
* x\_t = F x\_{t−1} + w\_t with F = \[\[1, 1\], \[0, 1\]\]
* z\_t = H x\_t + v\_t with H = \[1, 0\]
The Kalman filter gives me a smoothed price and a latent “velocity” that acts as a dynamic trend indicator. On top of that I layer:
* ADX + DI as a trend strength / direction filter
* ATR for volatility-scaled position sizing
* Trailing stops for exits
Implementation stack
* Backtrader for strategy engine and analyzers
* CCXT to fetch 1h OHLCV from Binance spot
* Custom Backtrader indicator for the adaptive Kalman filter
* QuantStats + Matplotlib for equity curve, drawdown and HTML report
Key details
* Universe: 10 USDC pairs (BTC, ETH, SOL, BNB, XRP, ADA, DOGE, AVAX, LINK, LTC)
* Timeframe: 1h bars, last \~90 days
* Position sizing: risk\_per\_trade \* equity / ATR, capped by max\_gross\_leverage
* Adaptive Kalman: periodically re-tunes Q and R via a small grid search on recent observations to minimize prediction MSE
* Entries:
* Long if Kalman velocity > threshold and +DI > −DI and ADX above trend threshold
* Short if Kalman velocity < −threshold and −DI > +DI and ADX above threshold
* Exits / reversals:
* Exit to flat when trend filter breaks
* Full reversal when signal flips direction (close and open in one order)
* Trailing stop (percentage-based) auto-attached on every entry
Results on this sample run
https://preview.redd.it/sjsx53ky415g1.png?width=1380&format=png&auto=webp&s=ad4e9eb846e0e168e416a17682337907c7e5f1c6
Initial capital: 100,000 (cash, no leverage)
* Final portfolio value: 155,373.74
* Sharpe ratio (daily, annualized): 1.74
* Sortino ratio: 3.73
* Max drawdown: 32.05%
* CAGR (annualized): \~238.8% (over the short window)
* Win rate: 48.9%
* Total trades: 328
The post includes:
* Full code for:
* Binance → pandas data fetcher using CCXT
* AdaptiveKalmanFilterIndicator with Q/R grid search
* Multi-asset portfolio strategy class
* Risk-based sizing, trailing stops, and signal logic
* QuantStats integration and custom Matplotlib equity/drawdown plot
* The equity curve vs BTC-USD benchmark and drawdown chart
* Discussion of how to extend it (regime switching, take-profit logic, futures, etc.)
Would be interested in feedback from others who have:
* Used state-space models (Kalman, particle filters, etc.) for trend/tracking in crypto
* Tried adaptive Q/R tuning in production (beyond simple grid search)
* Benchmarked similar approaches against more standard trend-following filters
If you have suggestions for making the Kalman part more statistically robust (e.g., EM for Q/R, multi-dimensional state including volatility, etc.), I’d love to hear your ideas.

.dark-mode .sxVvKW{ color: white; background-color: #333333; border: 2px solid white; }.light-mode .sxVvKW{ color: #333333; background-color: white; border: 2px solid #333333; }DOGEUSDT20241108C78000Dogecoin / Tether USD Nov 8 2024 78000.00 Callcrypto

DOGEUSDT20241108C78000
Dogecoin / Tether USD Nov 8 2024 78000.00 Call
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