Vivi_Quant

🚨 I understood why my bot was losing money.

After 2000+ setups :

👉 70% of the trades it rejected were winning.

⸻

So the problem wasn't the market.

💥 It was the selection.

⸻

I redid everything :

⚙️ new ranking based on actual performance
👻 analysis of unexecuted trades
📊 focus on what really works

⸻

🎯 Goal :

No longer trading more…
but trading better.

⸻

The real edge ?

👉 knowing what to ignore.

#trading #crypto #quant

📊 Journal de bord – Trading Bot (Day 8)

Today we continue the most important phase: data collection.

After several weeks of work, the bot is now running in real conditions with a simple objective: to identify an exploitable statistical edge.

Current status:
• 52 trades executed
• Winrate: 42%
• Average R/R: 1.28
• PnL: close to break-even

At this stage, the goal is not yet to “win big,” but to validate that the system behaves correctly live.

What is interesting for now:
• the bot manages risk correctly (partial TP + break-even)
• the detection of setups works
• some conditions already show promising performance

Meanwhile, the shadow tracker analyzes over 3200 missed opportunities, allowing us to study what the bot could have done differently.

This is exactly how you build a solid system:
analyze → measure → adjust → exploit the edge.

The research continues.

#quant #trading #edgeverse

$BANANAS31 My best trade thanks to my bot in 1 week! #quant #trading

🚨 My trading bot just revealed something crazy

After analyzing 2000+ market setups, the data showed something unexpected.

📊 70.5% of the rejected trades would have been winners.

Yes… you read that right.

The bot detects profitable opportunities very well.

But it rejects most of them because of one thing:

⚙️ Ranking score too low

So the problem might not be the strategy…

It might be the selection engine.

📈 Current live stats
• 32 trades
• 46.9% winrate
• PF 1.12

Still early, but the dataset is starting to reveal patterns.

Building a trading system is not about predicting the market.

It’s about discovering statistical edges hidden in data.

The experiment continues. 🤖📊

🤖 My trading bot just revealed something interesting…

After 362,000+ market scans and 26 live trades, the data is starting to talk.

📊 Current live stats
• Winrate: 38.5%
• Profit Factor: 0.41
• Expectancy: -0.57 USDC

At first glance… not impressive.

But here’s the interesting part 👇

👻 Shadow Tracker (simulated trades the bot DIDN’T take)
• Evaluated: 474 setups
• Would win: 265
• Would lose: 209
• Shadow WR: 55.9%

This means the market scanner is already detecting profitable patterns — but the ranking engine is still learning which ones to take.

⚙️ Strategy used:
Breakout + 1H retest with a quantitative ranking system.

Example from today:
🚀 VIRTUAL/USDC hit TP2 at 2.4R
Partial profit secured while the rest of the position continues trailing.

🧠 The goal right now is not profit yet.

The goal is data.

Because in quant trading the process is always the same:

Collect data → Find statistical edge → Optimize → Scale

Next milestone: 100 trades to validate the edge statistically.

If the data confirms it…
this bot might become a serious trading engine.

#QuantTrading #AlgoTrading #crypto

🚨 130 hours of trading. 293,000 setups analyzed. A strange signal appears.

I am currently developing a crypto quant bot that continuously analyzes the market.

In 130 hours it has already:

• scanned 293,000 market configurations
• filtered 52,000 valid trends
• identified 125 breakouts
• executed 18 real trades

But that’s not the most interesting part.

⸻

🧠 The data is starting to reveal a market bias.

When the bot enters too close to the breakout:

• Winrate ≈ 11%

When the entry is 0.5–0.75 ATR further away:

• Winrate ≈ 40%

➡️ Same setup. Radically different result.

⸻

💡 Hypothesis:

Immediate breakouts often capture:

• fakeouts
• liquidity grabs
• market noise

But when the movement has already gained momentum, continuation becomes statistically more likely.

In other words:

the exact timing of the entry could be the edge.

⸻

⚠️ Of course:

17 trades ≠ proof.

But this is exactly how quant funds discover edges.

They do not look for a magic setup.

They look for micro statistical biases in the data.

⸻

📊 This bot is designed for that:

• market filtering funnel
• setup ranking
• MFE / MAE analysis
• statistical buckets
• shadow tracking of rejected trades

Objective: let the data reveal the edge.

⸻

If this signal is confirmed after 100–200 trades, we could be facing:

➡️ an exploitable quant strategy.

And this is exactly how some strategies used by crypto desks are born.

⸻

I will share the results as I go along.

The market may be more predictable than we think.

#QuantTrading #algotrade #datadriven #CryptoQuant

📊 Bot Trading — Journal Data (Day 5)

• 14 trades
• Winrate : 28.6%
• Profit Factor : 0.32
• Drawdown : -3.9%
• Cumulative PnL : -14.3 USDC

🔎 274 750 analyzed opportunities → 16 trades executed

🧠 Multi-factor ranking used :
trend • volume breakout • relative strength • spread • entry distance • retest quality

📈 First statistical signals :

• NEUTRAL + BREAKOUT_1H_RETEST
7 trades • WR 0% • Expectancy -1.87 USDC

• DEFENSIVE + RETEST
4 trades • WR 75% • Expectancy -0.03 USDC

• NEUTRAL + RETEST
3 trades • WR 33%

📊 System quality (SQN) : -1.93
📈 Average MFE : +2.33%
📉 Average MAE : -2.92%

🎯 Minimum score recommended by the data : 64

Current objective : accumulate 100-200 trades to optimize the ranking and isolate the true edges.

#quant #TradingCommunity

Building a Quant Crypto Trading Bot — Day 5

Running fully live.

No backtests.
No simulations.
Only real market data.

Activity so far

• ~250k markets analyzed
• ~35k trends validated
• ~70 breakouts detected
• ~25 confirmed
• 11 trades executed

Current performance: losing streak.

But with only 11 trades, the sample size is meaningless.

Update today:
Improved the data collection engine.

The bot now logs more context for every decision:
rejected setups, ranking scores, market conditions.

Goal: build a reliable dataset to identify the real edge.

Mission: reach 100 trades → then analyze & optimize.
#TradingCommunity #crypto

📊 Logbook — Quant Bot | Day 4

The bot continues to operate in real conditions.

🔎 Current stats
• ~203k markets analyzed
• 28k trends validated
• 52 breaks detected
• 20 confirmed
• 7 trades executed

⚙️ Small optimization today:
RANK_MIN_SCORE 64 → 60

Objective: allow a bit more setups to accelerate data collection, without degrading quality.

The system remains very selective and waits for clean structures.

🎯 Current mission: reach 100 trades to start real statistical analysis.

Building a quant bot is mainly about:
patience + data + discipline.

#TradingCommunity #crypto

📊 Update Bot Trading — Day 3

My crypto trading bot, which I programmed myself, is currently running live on Binance.

Since the launch, the system has analyzed :

157,602 market configurations

To ultimately take only :

4 trades.

Why?
Because the bot applies several filters before entering a position.

📈 Current stats
Trades : 4
Winrate : 75%
Profit Factor : 2.21

Meanwhile, the bot continues to scan 130+ crypto pairs continuously.

I have added a screenshot of the bot's internal pipeline (Telegram) to show how the system filters setups in real time.

Objective:
Build a robust quant system and analyze several hundred real trades.

I document the bot's development publicly day by day.

Do you prefer a very selective bot with few trades or a more active bot?
#Quant #CryptoTrading #TradingBot #BuildInPublic

My crypto trading bot continues to operate live on Binance

Today: 2 trades executed.

The system continuously analyzes the market and only takes positions when several conditions are met: trend, compression, confirmed breakout, and entry on retest.

📊 Today's stats:
• 2 trades executed
• TP1 reached on one trade (+7% on the movement)
• partial profit secured
• stop moved to break-even
• trailing stop active to let the position run
Meanwhile, the bot continues to analyze the market:
• ~115 pairs scanned
• ~90 candidates analyzed
• 40+ breakouts monitored
• several retests pending
The majority of the setups are filtered.

The system remains deliberately very selective to avoid false signals.
Current objective: collect real market data and observe the system's behavior over several hundred trades.

#Quant #cryptouniverseofficial #TradeSignal $

My crypto trading bot just analyzed ~90,000 market configurations in 24h.

Result: 1 single trade.

Why?

Because I’m trying to build an extremely selective system, capable of avoiding the majority of false signals.

The system combines several modules:

📊 Market analysis

• trend filter

• minimum volatility (ATR)

• compression before breakout

• relative strength of altcoins vs BTC

• breakout confirmation with volume

🎯 Entry logic

• multi-timeframe breakout detection

• main entry on retest of level

• fallback momentum if the market does not retest

• alternative trend pullback setup

🧠 Opportunity selection

• scoring of setups

• ranking of opportunities

• selection of the best trades only

• automatic diversification by market clusters

📈 Advanced position management

• partial take profit

• stop moved automatically to break-even

• dynamic trailing stop

• pyramiding to strengthen winning positions

📡 Analytics

• complete trade tracking

• MFE / MAE analysis

• scoring of setups

• auto-learning system based on performance

In the last 24h:

≈ 90,000 analyzed configurations

23 breakouts detected

3 breakouts confirmed

1 valid retest

1 trade opened

Now the goal is simple:

let the bot run, accumulate real data, and gradually improve the edge.

#Quant #Crypto_Jobs🎯 #AlgorithmicTrading

Building a Crypto Trading Bot — Day 3

A few days ago I launched my first automated crypto trading bot on Binance.

The goal isn’t to trade all the time.

The goal is to trade only when the odds are truly in our favor.

So the bot is intentionally very selective.

It scans the market continuously and waits for several conditions to align before entering a trade:

• trend confirmation

• breakout structure

• volume expansion

• retest validation

• acceptable risk/reward

Bot activity so far

Pairs scanned

~90

Market configurations analyzed

100,000+

Breakouts detected

~40

Valid setups

~6

Trades executed

1

Yes — only one trade so far.

And that’s intentional.

Most trading bots fail because they overtrade.

This system is designed to do the opposite.

Recent improvements

• setup ranking system (the bot now scores every setup before trading)

• momentum breakout detection

• trend pullback entries

• portfolio exposure management

• dynamic trade management (BE + trailing)

The system is currently in the data collection phase.

Goal: reach 100+ trades to properly analyze performance.

Curious to hear your opinion:

What matters most for a trading system?

Winrate

or

Risk / Reward?

#AlgoTrading #QuantTrading #TradingBot #Crypto

My crypto trading bot has just analyzed over 55,000 market configurations in 24h…

…to take 1 single trade.

Why?

Because I’m trying to build an extremely selective system, capable of avoiding the majority of false signals.

After several weeks of development, I launched my first automated trading bot on Binance yesterday.

The bot is currently analyzing ~90 crypto pairs continuously and applies several filters before entering a position:

• trend filter
• market compression
• breakout detection
• entry only on retest
• automatic risk management

The system also integrates several advanced management mechanisms like position pyramiding, dynamic risk management, and performance tracking, in order to maintain the most disciplined approach possible.

📊 First statistics after 24h in real conditions:

• analyzed configurations: ~55,000
• breakouts detected: 14
• breakouts confirmed: 2
• trades executed: 1

The system is intentionally very strict in order to filter out the maximum number of false breakouts.

For now, the goal is mainly data collection.

I want to reach a minimum of 100 trades to analyze correctly:

• win rate
• drawdown
• market behavior

I will share here the statistics and the project's evolution over time.

Curious to hear your opinions:

👉 in your opinion, how many trades per day should a good trading bot take?

#AlgoTrading #CryptoTrading #TradingBot #Binance