Pyth Network (PYTH)
0.04
0.55
-92.7%
Stage 3 (Topping)
Bullish factors: 50d rising, MACD+
Bearish factors: price < 50d, price < 200d, death cross, far below high, distribution (OBV down, vol ratio 0.77)
Low: 0.03
Now: 0.04
Technical Snapshot
| RSI (14) | 46.4 | ADX (14) | 17.9 |
| 50d MA | 0.04 | 200d MA | 0.04 |
| Price vs 50d | ▼ Below | Price vs 200d | ▼ Below |
| Support | 0.04 | Resistance | 0.05 |
| ATR Volatility | 5.63%/day | Trend | SELL |
Crypto Performance Comparison
| Asset | 1 Month | 3 Months | 6 Months | 1 Year |
| PYTH | -16.5% | +30.4% | -0.8% | -43.6% |
| BTC | -4.2% | +3.0% | -5.4% | -31.3% |
| ETH | -2.5% | +16.4% | -8.2% | -39.9% |
| SOL | -3.2% | +19.4% | -6.2% | -45.8% |
Trend-Following Backtest
2-year simulation of 12,000 using 50d/200d MA crossover + RSI filter. Buy when price > 50d MA (rising) + RSI 40-75. Sell on death cross or RSI > 82.
Strategy vs Buy & Hold
| Asset | Strategy | Buy & Hold | Max DD | Trades | Win Rate |
| PYTH | -68.0% | -80.6% | -68.1% | 36 | 28% |
DCA vs Lump Sum (PYTH)
If you had deployed 12,000 using different timing strategies over the past year.
| Strategy | Return | Value Today |
| Lump Sum (1y ago) | -43.6% | 3,612 |
| DCA — 4 buys | -39.3% | 7,284 |
| DCA — 6 buys | -28.8% | 8,538 |
| DCA — 12 buys | -33.8% | 7,940 |
PYTH Deployment Plan — 12,000 Portfolio
Analysis by Elena Kowalski (Aggressive / Contrarian / Deep-Value). If you’re managing a 12,000 crypto allocation, here’s the plan:
| Position size | 3,000 (25% of portfolio) |
| Stop loss | 0.04 (-11.3%) |
| Target 1 | 0.00 (-100.0%) |
| Target 2 | 0.00 (-100.0%) |
| Entry quality | Pullback |
| Max concurrent positions | 4 |
Cash reserve: keep 25% buffer. Deploy in 2 tranches. Portfolio style: Aggressive / Contrarian / Deep-Value.
Backtest Trade Log
| Date | Action | Price | P&L |
| 2026-07-20 | BUY | 0.05 | |
| 2026-07-21 | SELL | 0.05 | -0.8% |
| 2026-07-22 | BUY | 0.05 | |
| 2026-07-23 | SELL | 0.05 | -5.4% |
| 2026-07-24 | BUY | 0.04 | |
| 2026-07-25 | SELL | 0.04 | -3.2% |
| 2026-07-26 | BUY | 0.04 | |
| 2026-07-27 | SELL | 0.04 | +1.5% |
| 2026-08-09 | BUY | 0.04 | |
| 2026-08-10 | SELL | 0.04 | +2.2% |
| 2026-08-11 | BUY | 0.04 | |
| 2026-08-12 | SELL | 0.04 | -3.4% |
Trend-following methodology: 50d/200d MA crossover + RSI filter + ADX regime gate
Data via Yahoo Finance / CoinGecko · Not financial advice. For educational purposes only.
92.7% drawdown from 0.55 to 0.04 and they still classify this as Stage 3 not Stage 4. at what point do we just call it what it is
oracle_skep_ Stage 3 vs 4 distinction matters for allocation strategy. a token with real adoption like Pyth can bounce from Stage 3 but Stage 4 means structural failure. the 0.04 price doesnt tell the full story
adoption saving the token requires revenue to eventually reach holders. until pyth commits to buybacks or burns its just a company with a tracking token attached
alloc the tracking token framing is right. feed revenue is real, holders just arent in the waterfall until a burn or buyback exists. until then its equity cosplay
equity cosplay is exactly it. the token is a fan club membership until the revenue waterfall includes holders
every infra token promises the buyback is coming and ships a governance vote instead. been holding my breath on that since 2021
pyth will run a revenue vote, it passes with 6% turnout, implementation lands two quarters late. the 2021 infra playbook on a loop
6 percent turnout on a vote that decides where every fee dollar goes is the most pyth thing possible. whales vote, bagholders watch
the 2021 infra playbook includes the part where the buyback finally ships after a 95 percent drawdown and nobody is left to care
92.7% drawdown and still classified as Stage 3 Topping not Stage 4? feels generous for a token at $0.04
oracled_out 92.7% drawdown and Stage 3 not 4 is generous. at 0.04 with constant token unlocks this is a slow bleed not a bounce setup
stage assignments are model outputs not prophecies. i got burned shorting a stage 3 that mean reverted, the labels pretend to precision the underlying doesnt have
getting run over shorting a stage 3 is a sizing problem more than a label problem, but yeah the precision on those stages oversells the certainty
pyth has real adoption for price feeds but the token itself just dilutes. revenue doesnt mean much when supply keeps growing
Yuna K. pyth has real adoption for price feeds but token holders get nothing. revenue and token value are completely disconnected here
pyth feeds on every perp desk and the token at 0.04 is the cleanest proof that b2b oracle revenue and retail token bags are different asset classes
the SELL is whatever. the interesting bit is pyth powering more price feeds than people realize while the token sits at 0.04. product adoption and token price fully decoupled
entropy the classic infra paradox, every perp desk consumes the feeds and nobody buys the token. the product prints and the chart bleeds
a 12000 deployment plan attached to a SELL rating is doing more work than the thesis. scaling into weakness needs more than stage labels
12k scaled into a SELL rating is a hedge against being wrong, which is just paying rent on indecision
either your model says short it with size or it says stand aside. 12k averaged into a SELL is a coin flip with extra steps
the 12k isnt conviction its budget. these plans are sized so being wrong is a rounding error and being right is content
the stage labels are the whole product tho. precision theater sells subscriptions even when the p and l says do nothing
price feeds on every perp desk and the token at 0.04. pyth proved oracles can be a business, it just hasnt proved the token is that business
revenue vote passing with 6 percent turnout means the buyback covers exactly the holders who voted. the other 94 percent learn from the press release
every perp desk i use has pyth feeds somewhere in the stack and the token sits at 4 cents. at some point that gap closes, question is which direction closes it