DOGE Reddit Mentions | ChartExchange

Honestly the instinct to avoid textbook indicators is half the battle — most of them are public precisely because the edge got priced out decades ago. A few things from what's worked for me, nothing secret.
Start from a question, not a chart. The wrong starting point is "what do RSI and MACD do together?". The right one is "when a large holder accumulates for 3 days straight, what does price do?". Your job is to find a data transformation that answers that question. The transformation is the indicator. Textbook indicators exist because someone once had a question they answered — but those questions are answered to death.
Use data most retail traders don't look at. Price is the worst feature because it's the one everyone stares at. The non-price data that tends to have edge: funding rates, open interest changes, on-chain flows, order book imbalance, cross-asset relationships (BTC dominance, basis, term structure), stablecoin supply. You don't need all of them — you need one that actually moves before price does.
Check orthogonality to what already exists. After you build something, run its rolling correlation against BTC return, against 20-day momentum, against RSI. If your indicator is roughly 95% correlated with a textbook signal, you have not built anything new. If it's under 30% correlated, you might have something. Low correlation doesn't mean profitable — but high correlation means you're re-discovering known features.
Normalize so you can compare. Raw funding is different for every coin. Raw OI is different for every coin. Convert everything to z-scores, percentiles, or ratios relative to the asset's own recent history, so "funding is very high for BTC" and "funding is very high for DOGE" mean the same thing without conflating them.
Test against noise, not against curves. The cheapest thing to filter bad indicators: randomize the input to your indicator (shuffle in time, or replace with noise) and verify the strategy's returns collapse. If they don't collapse, your "signal" was probably just fitting the curve of the market itself, not extracting anything from the input.
Resist complexity. My honest experience is that indicators which survive OOS are mostly simple transformations of one or two clean inputs. Three-layer nested conditions with magic thresholds are where overfits live. If you can't describe your indicator in one sentence, it's probably overfit.
Write everything down. Keep a file of every hypothesis you tested, whether it worked or not. Most won't. The failed ones are informative.
Also — almost all edges you'll find solo will sit in the 1x Sharpe range. That's still valuable. Anyone selling you a 3+ single-indicator setup is selling something.

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