cryptotradehead

Win rate is the easiest number to make look good and the least informative one to report. Anyone showing you a win rate without the size of the losses is showing you the half of the picture that flatters them.

Here is what that came from.

A strategy with a 96% win rate lost me money. Win rate tells you almost nothing.

I backtested a martingale system - add to losers, close when it recovers. 75 of 78 trades were winners.

It still lost 519.83 USDT, because the three losing trades gave back more than all 75 winners made combined.

Peak margin on those trades reached 4x the entire account.

Part of a public experiment: 5,063 USDT to 1,000,000 USDT, every result published - including the ones that make me look bad.

Run on $BNB

#Binance #Futures #Backtesting #TradingBot #CryptoData

DAY 1 - 5,063 USDT to 1,000,000 USDT

Balance: 5,063.23 USDT
Change: +0.00 USDT (+0.0%)

Runners stopped while I rebuild the strategy from the backtest evidence. Nothing open.

Running on a Binance testnet account, so this is demo capital - I would rather say that than let you assume otherwise. The strategy, the costs and the losses are all real.

No signals for sale, no course, no affiliate link. Just the account and the data behind it.

Traded in this challenge: $BTC $SOL $AVAX

#Binance #USDT #Futures #TradingChallenge #Crypto

24-HOUR SNAPSHOT

SOL/USDT: 93.50 USDT (-1.30%)
BNB/USDT: 704.00 USDT (-1.16%)
BTC/USDT: 77,509.90 USDT (-0.77%)
ETH/USDT: 2,270.08 USDT (+0.07%)

Funding on SOL/USDT is +0.0100% per 8h.

Context, not a call: I do not post entries or targets. Everything I trade is logged and published after the fact, wins and losses both.

Covered above: $SOL $BNB $BTC

#Binance #Crypto #Futures #BTC #Altcoins

DAY 1 - 5,063 USDT to 1,000,000 USDT

Balance: 5,063.23 USDT
Change: +0.00 USDT (+0.0%)

Runners stopped while I rebuild the strategy from the backtest evidence. Nothing open.

Running on a Binance testnet account, so this is demo capital - I would rather say that than let you assume otherwise. The strategy, the costs and the losses are all real.

No signals for sale, no course, no affiliate link. Just the account and the data behind it.

Traded in this challenge: $BTC $SOL $AVAX

#Binance #USDT #Futures #TradingChallenge #Crypto

24-HOUR SNAPSHOT

BNB/USDT: 702.20 USDT (-1.83%)
BTC/USDT: 77,454.00 USDT (-0.64%)
ETH/USDT: 2,271.06 USDT (+0.23%)
SOL/USDT: 93.45 USDT (-1.37%)

Funding on BNB/USDT is +0.0076% per 8h.

Context, not a call: I do not post entries or targets. Everything I trade is logged and published after the fact, wins and losses both.

Covered above: $BNB $SOL $BTC

#Binance #Crypto #Futures #BTC #Altcoins

A backtest assumes your stop-loss always exists. Live, it is an order that can be refused. The gap between those two assumptions is where real money goes.

Here is what that came from.

My stop-loss order got rejected mid-crash. The bot then opened 3 more positions in 6 seconds.

During a fast drop, Binance rejected my stop-loss with error -2021: the market had already moved past the trigger price before the order landed.

My code did not handle that rejection. The position stayed open with no stop, and because the bot's own state never recorded it, the next signal opened another position on top. Three times.

Intended risk was 3 USDT. Actual loss was 11.75 USDT.

Part of a public experiment: 5,063 USDT to 1,000,000 USDT, every result published - including the ones that make me look bad.

Run on $BNB

#Binance #Futures #Backtesting...

Any strategy must clear 0.240% of position size per trade just to break even. Most published setups never mention this number, which is why most published setups lose money.

Here is what that came from.

I measured real execution costs with 48 controlled trades. One number decides everything.

I stopped assuming what trading costs and measured it: 48 controlled round trips, entering and exiting immediately, recording the actual fill against the reference price.

Fee was constant at 0.04% per side. Slippage was not - ETH filled at essentially zero, while DOGE cost 50 to 100 basis points per round trip.

On a 150 USDT position, a round trip costs 0.36 USDT before the market moves at all.

Part of a public experiment: 5,063 USDT to 1,000,000 USDT, every result published - including the ones that make me look bad.

Run on $ETH $DOGE

#Binance #Futures #Backtesting #TradingBot #CryptoData

DAY 1 - 5,063 USDT to 1,000,000 USDT

Balance: 5,063.23 USDT
Change: +0.00 USDT (+0.0%)

Runners stopped while I rebuild the strategy from the backtest evidence. Nothing open.

Running on a Binance testnet account, so this is demo capital - I would rather say that than let you assume otherwise. The strategy, the costs and the losses are all real.

No signals for sale, no course, no affiliate link. Just the account and the data behind it.

Traded in this challenge: $BTC $SOL $AVAX

#Binance #USDT #Futures #TradingChallenge #Crypto

HOW I TESTED IT

Donchian 20 channel, ATR stop, 10 symbols, four and a half years.

If this were just riding a bull market, the longs would be the winners. They are not - they are negative.

The shorts even made money on coins that ROSE over the period. BTC gained 94% buy-and-hold, and shorting its breakdowns still returned +22.83 USDT. XRP gained 87%, shorts returned +24.27 USDT.

THE NUMBERS
Short trades: +134.12 USDT
Long trades: -17.83 USDT
Total trades: 3,209
Profitable short symbols: 8 of 10

Crypto falls faster than it rises - liquidation cascades are asymmetric. That is documented market structure, which is why this result is more believable than one that only works when everything goes up.

Part of a public experiment: 5,063 USDT to 1,000,000 USDT, every result published - including the ones that make me look bad.

Run on $BTC $XRP $LTC

#Binance #Futures #Backtesting #TradingBot #CryptoData

This is genuinely different from a useless signal - there is information here. But to use it you would need to know the regime in advance, which is the same circular problem that kills everything else.

Here is what that came from.

Funding rate predicts where price goes. It just changes which direction, by year.

I sorted every funding print into quintiles and measured the forward 72-hour return of the highest versus the lowest.

In bear markets, high funding preceded WORSE returns - crowded longs getting squeezed. From 2024 onward it preceded BETTER returns - momentum.

The signal is real. The sign is not stable. Average it across all years and you get -0.08%, below the 0.240% it costs to trade.

Part of a public experiment: 5,063 USDT to 1,000,000 USDT, every result published - including the ones that make me look bad.

Run on $BTC $ETH $DOGE

#Binance #Futures #Backtesting #TradingBot #CryptoData

DAY 1 - 5,063 USDT to 1,000,000 USDT

Balance: 5,063.23 USDT
Change: +0.00 USDT (+0.0%)

Runners stopped while I rebuild the strategy from the backtest evidence. Nothing open.

Running on a Binance testnet account, so this is demo capital - I would rather say that than let you assume otherwise. The strategy, the costs and the losses are all real.

No signals for sale, no course, no affiliate link. Just the account and the data behind it.

Traded in this challenge: $BTC $SOL $AVAX

#Binance #USDT #Futures #TradingChallenge #Crypto

DAY 1 - 5,063 USDT to 1,000,000 USDT

Balance: 5,063.23 USDT
Change: +0.00 USDT (+0.0%)

Runners stopped while I rebuild the strategy from the backtest evidence. Nothing open.

Running on a Binance testnet account, so this is demo capital - I would rather say that than let you assume otherwise. The strategy, the costs and the losses are all real.

No signals for sale, no course, no affiliate link. Just the account and the data behind it.

Traded in this challenge: $BTC $SOL $AVAX

#Binance #USDT #Futures #TradingChallenge #Crypto

It is far too small to build a strategy on - 0.0042% against 8-hour price swings of 2.36%. But if you are already holding a position, the direction of funding is not the same on every coin, and most people assume it is.

Here is what that came from.

On most coins longs pay shorts. On BNB it is the other way round.

Funding is the payment between long and short holders of a perpetual. Positive means longs pay shorts, which is the usual state of a market where most people are betting up.

Across 34,431 prints, BNB averages NEGATIVE funding - shorts pay longs - and only 22% of its prints are positive. BTC, ETH and DOGE are all positive.

So holding a long BNB perp is paid to exist, where a long BTC perp pays rent.

Part of a public experiment: 5,063 USDT to 1,000,000 USDT, every result published - including the ones that make me look bad.

Run on $BNB $BTC $ETH

#Binance #Futures #Backtesting #TradingBot #CryptoData

Adaptive rules feel like the answer to regime change. They are usually just a slower version of the same bet, with the losses already paid for the signal.

Here is what that came from.

I tried to make the bot detect when momentum flips. It cannot be done in time.

Momentum inverted in 2026. The obvious fix: watch recent performance, and flip to the opposite rule when momentum stops paying.

I tested that over six different lookback windows. The best one returned +1.783% per rebalance against a +1.737% baseline - a difference indistinguishable from noise. Every other window was worse. None repaired 2026.

The reason is simple: by the time trailing performance tells you the regime flipped, you have already taken the losses that told you.

Part of a public experiment: 5,063 USDT to 1,000,000 USDT, every result published - including the ones that make me look bad.

Run on $SOL $BTC

#Binance #Futures #Backtesting #TradingBot #CryptoData

24-HOUR SNAPSHOT

BNB/USDT: 707.15 USDT (-1.05%)
BTC/USDT: 77,952.50 USDT (-0.17%)
ETH/USDT: 2,286.39 USDT (+0.67%)
SOL/USDT: 94.10 USDT (-1.54%)

Funding on SOL/USDT is +0.0100% per 8h.

Context, not a call: I do not post entries or targets. Everything I trade is logged and published after the fact, wins and losses both.

Covered above: $SOL $BNB $ETH

#Binance #Crypto #Futures #BTC #Altcoins