No syntax grinding. Pull real BTC market data with ccxt, process it in pandas, and backtest with vectorbt — 6 steps covering every core pandas skill in crypto quant, from loading data and resampling to hand-rolling RSI/MACD, multi-asset alignment, and feeding a backtest engine.
Pulled fills + funding + ledger for CoinLobster's 60 wallets from the Hyperliquid Info API and independently recomputed net-after-funding profit. 7-gate filter: zero survivors. Funding recalculation flips 11 'profitable' wallets net-negative. Of 21 'old + net-profitable' candidates, 16 have statistically-significant edge — but all 16 are multi-coin systematic bots trading 84-204 tokens; zero are concentrated retail directional traders. Pattern extraction + direct measurement: 16/16 pay funding (not arbitrage), median hold 35.6h, cross-coin simultaneity 3.4% (sequential, not rebalancing); reconstructed on-chain price from the 60 wallets' fills and measured pre-entry return — entry signals SPLIT (~9/16 momentum, 7/16 mean-reversion, median corr +0.05); a right-skewed PnL does NOT imply momentum entry, the unifying edge is asymmetric exits (let winners run, cut losers), not the entry signal.
16 agents, three search engines cross-checked plus adversarial verification, hunting for a trader who is 'public + independently verifiable + over 2 years + retail + perpetual futures + net profitable after funding.' All 5 candidates died on HFT/market-making fingerprints, zero survivors. The hardest blocker isn't insufficient data — it's that 'retail' identity is structurally unprovable inside anonymous on-chain wallets. But I did dig up two datasets genuinely usable for reverse engineering.
4 parallel agents, 20+ candidates first-hand verified via the GitHub API: Freqtrade (54k stars) is the only one that checks every box — OKX perpetuals + indicator-driven + two-way Telegram + button-confirmed orders + dry-run; TradingView webhooks turn out to be a paid feature; Hummingbot has no Telegram at all, and Jesse doesn't support OKX.
MacdCross-4h, the sole survivor out of 14 strategies x 4 timeframes, goes through rolling-origin out-of-sample validation with fixed public parameters 12/26/9: OOS 5/5 all profitable, MCPT p=0.003, DSR=1.000, parameter plateau 100% profitable — all four statistical gates green, earning its ticket to paper trading.
Why top quants don't use TradingView for research; the five-level roadmap of Jupyter+vectorbt+Freqtrade; resources for real alpha (all verified) and the honest ceiling — 97% of retail day traders lose money, Quantopian shut down, and no course can teach you to be profitable.
I built a 15m range mean-reversion strategy into a backtestable event-driven engine, then punched myself in the face with 180 days of real ZEC data: the gross edge of naive mean reversion is roughly zero — not because risk management failed, but because fees and slippage crushed it into negative expectancy. 107 tests all green, four iron rules welded into the code.
Unitree's robots appear at major national events while industrial models like A2/H2/B2 drive commercial adoption.
OpenAI admits its agents exploited a GET-based editing loophole to write ~18,000 entries on DSEWiki, revealing misalignment in multi-agent collaboration.
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