pythontrading

Tired of using generic crypto portfolio apps that charge you $15/month for a "premium" layout? If you want absolute control over your data, it's time to build your own centralized management system.
Today, we are combining the layout power of Notion with the data power of the Binance API. We are going to write a Python script that automatically fetches your real-time balances and prepares them to be synced directly into a custom Notion database.
No more manual data entry. Total automation.
Step 1: The Setup
You will need two things:
1. Your Binance API keys (Read-only permissions!).
2. A Notion account with a blank Database created.
Note: For the full integration, you would also create a Notion Integration API key, but today we are building the Binance data extraction engine.
Step 2: Extracting Clean Portfolio Data
When you pull your balance from Binance, it gives you a massive list of every coin on the exchange, including tiny dust amounts. We need to write a script that filters this data so we only send our actual holdings to Notion.
Make sure you have the ccxt library installed (pip install ccxt).
import ccxt
# 1. Connect to your Binance account
# SECURITY REMINDER: Never share these keys.
binance = ccxt.binance({
'apiKey': 'YOUR_API_KEY_HERE',
'secret': 'YOUR_API_SECRET_HERE',
'enableRateLimit': True,
})
try:
# 2. Fetch the raw balance data
raw_balance = binance.fetch_balance()

# 3. Create a clean dictionary for our Notion database
clean_portfolio = {}

# 4. Filter out empty balances and "dust"
for coin, amount in raw_balance['total'].items():
if amount > 0.001: # Adjust this threshold to hide dust
# Here we fetch the current USDT price for the coin
try:
ticker = binance.fetch_ticker(f"{coin}/USDT")
current_price = ticker['last']
usd_value = amount * current_price

# Only save coins worth more than $1 to our dashboard
if usd_value > 1.0:
clean_portfolio[coin] = {
'amount': round(amount, 4),
'usd_value': round(usd_value, 2)
}
except:
pass # Skip coins that don't have a direct USDT pair

print("✅ Data extracted and cleaned. Ready for Notion Sync:")
for coin, data in clean_portfolio.items():
print(f"{coin}: {data['amount']} coins | Value: ${data['usd_value']}")
except Exception as e:
print(f"Error: {e}")
Step 3: Why This System Wins
Once this data is extracted, the next step is using the requests library to POST this directly to your Notion Database URL.
Why build this?
• Privacy: Your portfolio data stays between you, Binance, and your private Notion workspace. No third-party tracking apps.
• Customization: In Notion, you can build custom formulas around this data—calculate taxes, set visual goals, or track your portfolio against your real-life expenses.
Do you want Part 2, where we write the exact API code to push this data into the Notion tables? Drop a "+" in the comments if I should drop the rest of the code! 👇
#Notion #BinanceAPI #PortfolioTracker #PythonTrading #TechInCrypto

Most beginners look at technical indicators like magic lines on a chart. They wait for a line to cross another line and click "Buy." But if you don't understand the why behind the formula, you are just gambling.
Today, we are combining pure logic with Python. We are going to build a script that calculates the RSI (Relative Strength Index) for $BTC or $ETH , but first, let's understand what we are actually coding.
Step 1: The Conceptual Math of RSI
RSI is a momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100.
Traditionally:
• Over 70: The asset is considered "Overbought" (due for a pullback).
• Under 30: The asset is considered "Oversold" (due for a bounce).
But why? The math behind RSI simply compares the magnitude of recent gains to recent losses over a specified time period (usually 14 periods).
If the average of your recent up-closes is much higher than the average of your down-closes, the RSI goes up. It's a mathematical representation of buyer vs. seller exhaustion. We aren't predicting the future; we are calculating the current mathematical probability of a trend reversal.
Step 2: The Python Code
To calculate this automatically, we will use our trusty ccxt library to get the data, and pandas_ta (a technical analysis library) to do the heavy math.
Install the required libraries first: pip install ccxt pandas pandas_ta
Here is a clean, conceptual script to get the current RSI of $BTC :

import ccxt
import pandas as pd
import pandas_ta as ta
import time
# Settings
SYMBOL = 'BTC/USDT'
TIMEFRAME = '15m' # 15-minute candles
LIMIT = 100 # We need enough candles to calculate the 14-period average
# Initialize exchange
exchange = ccxt.binance()
def get_rsi(symbol, timeframe, limit):
try:
# 1. Fetch OHLCV data (Open, High, Low, Close, Volume)
bars = exchange.fetch_ohlcv(symbol, timeframe, limit=limit)

# 2. Convert to a Pandas DataFrame
df = pd.DataFrame(bars, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

# 3. Calculate RSI using the close price (default length is 14)
df.ta.rsi(close='close', length=14, append=True)

# 4. Get the last (current) RSI value
current_rsi = df['RSI_14'].iloc[-1]
current_price = df['close'].iloc[-1]

return current_price, current_rsi

except Exception as e:
print(f"Error fetching data: {e}")
return None, None
# Run the check
price, rsi = get_rsi(SYMBOL, TIMEFRAME, LIMIT)
if rsi:
print(f"Current {SYMBOL} Price: ${price}")
print(f"Current RSI (14): {rsi:.2f}")

if rsi < 30:
print("🚨 MATHEMATHICAL SIGNAL: RSI is Oversold (<30). Potential buying opportunity.")
elif rsi > 70:
print("🚨 MATHEMATHICAL SIGNAL: RSI is Overbought (>70). Potential selling opportunity.")
else:
print("Neutral zone. Let the code wait.")

Why This Beats Manual Trading
By running this script (or combining it with the Telegram bot from our previous article), you remove emotion entirely. You act strictly on mathematical data. No FOMO, no panic.
Challenge for you: Can you modify this code to check $ETH and $SOL at the same time? Let me know in the comments if you want the multi-coin version tomorrow! 👇
Disclaimer: This is for educational purposes. RSI is a probability tool, not a guarantee. Always manage your risk.

#PythonTrading #CryptoMath #RSITrading #BinanceAPI #AlgoTrading #BTC #ETH

Waking up at 3 AM to check if BTC broke resistance? Constantly refreshing the Binance app while having dinner? We’ve all been there. It’s exhausting.
In our last post, we connected to the Binance API. Today, we are taking it a step further. We are going to build a simple Python bot that watches the market for you and sends a Telegram message directly to your phone when a coin hits your target price.
No more FOMO. Let the code do the waiting.
Step 1: Set Up Your Telegram Assistant
Before we write Python, we need a bot on Telegram.
1. Open Telegram and search for @BotFather (the official bot creator).
2. Send /newbot and follow the prompts to give your bot a name and username.
3. BotFather will give you a HTTP API Token. Copy this! Treat it like a password.
4. Now, search for your new bot in Telegram and click "Start".
5. Next, search for @userinfobot and forward a message to it (or just start it) to get your Chat ID (a string of numbers).
Step 2: The Logic Behind the Code
We are going to use the ccxt library to fetch the price, and the standard requests library to send the message to Telegram.
The core logic is a continuous loop (while True). The script asks Binance for the price, checks if it hit our target, and if not, it "sleeps" for a few seconds before asking again. This prevents us from spamming the exchange with requests.
Step 3: The Python Code
Make sure you have the libraries installed: pip install ccxt requests

import ccxt
import requests
import time
# --- YOUR SETTINGS ---
TELEGRAM_TOKEN = 'YOUR_BOT_TOKEN_HERE'
CHAT_ID = 'YOUR_CHAT_ID_HERE'
SYMBOL = 'BTC/USDT'
TARGET_PRICE = 65000 # The price you are waiting for
# Initialize Binance (No secret keys needed for public price data!)
binance = ccxt.binance()
def send_telegram_alert(message):
"""Sends a message via Telegram API"""
url = f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendMessage?chat_id={CHAT_ID}&text={message}"
requests.get(url)
print(f"🤖 Bot started. Monitoring {SYMBOL}...")
while True:
try:
# Fetch the latest price
ticker = binance.fetch_ticker(SYMBOL)
current_price = ticker['last']
print(f"Current {SYMBOL} price: {current_price}")
# Check if target is hit
if current_price >= TARGET_PRICE:
msg = f"🚨 ALERT! {SYMBOL} just crossed {TARGET_PRICE}! Current price is {current_price}."
send_telegram_alert(msg)
print("Alert sent! Pausing for 1 hour to avoid spam...")
time.sleep(3600) # Sleep for 1 hour after sending an alert
# Wait 10 seconds before checking again
time.sleep(10)

except Exception as e:
print(f"Connection error: {e}")
time.sleep(10) # If network fails, wait and try again

Why This is Powerful
This is a basic structure, but think about the possibilities. You can easily modify this script to:
• Alert you when a coin drops below a certain price (Buy the dip!).
• Monitor 10 different coins at the same time.
• Add an RSI indicator to alert you when a coin is "Oversold".
What should we add to this bot next? RSI alerts, or moving average crossovers? Let me know in the comments below! 👇
Disclaimer: Educational purposes only. Always test scripts thoroughly before relying on them.
#TelegramBot #PythonTrading #CryptoAlerts #BinanceAPI #cryptoeducation #AlgoTrading #TechInCrypt

Still staring at the screen all day, waiting for that perfect entry point? 📉 Stop it. Welcome to the other side: The world where code works and traders relax (mostly).
Using the Binance API (Application Programming Interface) is like giving your strategy a direct, high-speed connection to the exchange. It’s not just for advanced quant traders; it’s for anyone tired of manually refreshing charts.
In this first article, let’s make it real. Here’s a basic, step-by-step guide to getting your first taste of crypto automation. No fluff.
Step 1: Why Bother with API? (The "Secret Sauce")
You might be thinking, "I can just place orders on the app." Why the API?
1. Speed: Code is faster than your thumb. Much faster.
2. No Emotion: A script doesn't panic-sell when it sees a red candle. It follows instructions.
3. Scale: A human can only monitor 2-3 charts at once. A script can check hundreds of pairs in milliseconds.
4. 24/7: Your script doesn’t sleep, eat, or have social life.
Step 2: The Security Baseline (READ THIS!)
The biggest mistake beginners make is security. When you generate an API key on Binance:
• NEVER share your API Secret. Treat it like your private seed phrase. If anyone gets it, they can access your account.
• Restrict IP Access: In the Binance API settings, only allow access from your specific IP address. This is a game-changer.
• Disable Withdrawals: Unless absolutely necessary for a specific reason, keep withdrawal permissions for the API disabled. A typical trading script only needs "Enable Reading" and "Enable Spot & Margin Trading".
Step 3: Getting Your Keys on Binance
Let's get the keys to the castle.
1. Log into your Binance account on the web
2. Click on your profile icon and select API Management.
3. Click Create API Key. Give it a label (e.g., "Automation Start").
4. You’ll get an API Key and an API Secret. Copy them immediately and store them securely. Once you close this window, the secret will be masked.
5. Click Edit Restrictions and set up the permissions (as mentioned in Step 2). Add your IP restriction.
Step 4: Your First Request (Python Example)
To keep it simple, we'll use Python and the powerful ccxt library, which standardizes crypto API calls. Install it: pip install ccxt
Here’s the absolute minimum to fetch your BTC balance without manual trading.
import ccxt
# 1. Initialize the exchange (Replace with YOUR keys)
# Note: In a real bot, use environment variables to hide keys!
binance = ccxt.binance({
'apiKey': 'YOUR_ACTUAL_API_KEY_HERE',
'secret': 'YOUR_ACTUAL_API_SECRET_HERE',
'enableRateLimit': True,
})
# 2. Make the API call to get account info
try:
balance = binance.fetch_balance()
# 3. Print only the BTC portion for clarity
btc_balance = balance['total'].get('BTC', 0)
print(f"Your BTC balance is: {btc_balance}")
except Exception as e:
print(f"An error occurred: {e}")

Congratulations! You’ve just communicated directly with the exchange using code. This is the moment your trading changes.
What's Next?
This was just to open your balance. What should we build next?
• A basic alert system for sharp price moves?
• An automated dollar-cost averaging (DCA) bot?
• A "panic sell" button that triggers in code?
Let me know in the comments. This is only the beginning.
Disclaimer: Not financial advice. The CCXT library is a third-party tool; use at your own risk. Automating trading involves risk, including the loss of capital. Always backtest and test on testnets.
#BinanceAPI #AlgoTrading #PythonTrading #cryptobot
#CryptoEducation #tradingtips #TechInCrypto #LearnAndEarn #CryptoStrategy

In trading, many strategies look great… until they face real market conditions.
That’s why backtesting is essential.
A proper backtest allows you to:
Measure not only ROI, but also risk (especially max drawdown).Understand how a strategy performs in different market regimes.Avoid overconfidence from “lucky trades”.
As a data scientist, I build Python trading bots with a simple philosophy:
👉 Returns are important, but risk management defines survival.
I use machine learning to optimize parameters, test setups, and evaluate the trade-off between higher returns and controlled drawdowns. My focus is not on predicting the future perfectly, but on creating strategies that remain robust when conditions change.
This account will share my journey in algorithmic trading: lessons learned, insights on risk management, and how data-driven approaches can give traders an edge.
Do you backtest your strategies, or do you rely more on intuition when trading?

— DrLegend —

#Backtesting #AlgorithmicTrading #machinelearning #Aİ #PythonTrading

Over the past few weeks, I've developed and refined an AI-powered Python signal generator bot to automate strategy detection and enhance trade decision-making — and I’m excited to share some impressive results and takeaways.
🤖 How It Works:
The bot scans live market data and uses a combination of candlestick patterns, price action zones, and multi-timeframe (MTF) trend analysis to generate actionable trade signals. It supports strategies like:
Bearish/Bullish EngulfingTweezer Tops/BottomsShooting StarMorning StarPeak Rejection (Zone & Candlestick based)Daily High/Low ZonesOnce identified, signals are ranked using key metrics like:Win RateProfit FactorPnL (Profit and Loss)Trade History & Breakdown
The bot then visualizes all this in a dashboard for live monitoring and rapid execution decisions.
📈 Strategy Leaderboard Highlights:
From the recent analysis:
Bearish Engulfing (Apex) stood out with 14 trades, 13 wins, 92.86% win rate, and a Profit Factor of 11.99, netting a total PnL of +268.78.
The Tweezer Top strategy remains perfectly accurate so far, with a 100% win rate across multiple trades.
Peak Rejection (Shooting Star) showed a decent 62.5% win rate and solid returns at +58.55 PnL.
These results help me filter and double down on what works — and cut underperforming strategies fast.
🔄 Live Bot in Action:
The bot is currently running real-time trades with auto-evaluation of MTF trend alignment and risk management via SL/TP. Here’s what it handles:
Leverage-specific entries per signalTrailing stop loss (TSL) handling and breakeven exits
Live position tracking with PnL and TP hit logs

Today’s top trade? A SHORT on MILKUSDT using Peak Rejection (Tweezer Top) with a whopping +45.36% PnL. ✅
🧠 Why Use This Bot?
✅ Cuts emotional bias from trading✅ Fast strategy iteration with real data feedback✅ Easy integration with Binance API✅ Helps focus only on high-probability setups
🛠️ Built With:
Python (Pandas, NumPy, TA-Lib, Plotly)Binance API (for live price & execution)Custom Signal Ranking & AI-generated Strategy Reports#PythonTrading #cryptosignals #BinanceTrading #BitcoinTreasuryWatch #BTCReserveStrategy @IRONMIND_BR @Square-Creator-861122001 @BLACKEAGLE_BESIKTAS @properlogic
$MEMEFI $PROVE $MYX