Stop dreaming about strategies. Start building them. 🛠️ Introducing the Anatomy of the Qvis Strategy Builder —the ultimate workspace where trading ideas become intelligent algorithms. Here's how we bridge the gap from concept to execution: 1️⃣ The Conversational Hub : Describe your trading ideas in plain English. Your AI Agent acts as your dedicated quantitative co-pilot. 🗣️✈️ 2️⃣ System Memory & Iteration : The Agent retains a long-term memory of your distinct trading style, risk preferences, and past iterations. It gets smarter with you. 🧠🔄 3️⃣ Real-Time Scripting : Instantly generate backtest-ready Python code. Edit it manually or prompt the AI for a rapid refine. 💻⚡ The future of quant trading is conversational. Learn more: qvis.ai #QvisAI #QuantTrading #AITrading #NoCodeTrading #PythonTrading

Do you plan your trades? #algotrading #algorithmictrading #quant #quanttrading #algo #pythontrading #backtesting #tradingbot #autotrading #quantfinance

Save this post for reference later. Learn hands-on with Quantra: quantra.quantinsti.com/cours… Go deeper with EPAT: quantinsti.com/epat #OptionsTrading #MachineLearning #AlgorithmicTrading #QuantTrading #AlgoTrading #PythonTrading #Backtesting #TradingEducation

Options trading gets better when it becomes a process, not a hunch. Data -> signal -> strategy -> execution -> backtest. That is where ML adds value. Save this post for reference later. Quantra: quantra.quantinsti.com/cours… EPAT: quantinsti.com/epat #OptionsTrading #MachineLearning #AlgoTrading #QuantTrading #PythonTrading

🚨 Quant Trading Serisinin 4. Bölümü yayınlandı! 🔥 SMA Crossover, Momentum ve Mean Reversion… Algoritmik trading’in temel taşları olan bu **üç klasik stratejiyi** Python ile sıfırdan kodlayıp gerçek BTC verisi üzerinde test ediyoruz. Bu makalede backtest yapmıyoruz (o 5. bölümde). Burada odak noktamız: stratejilerin mantığını anlamak, temiz kod yazmak ve en yaygın hatalardan kaçınmak. ### Veri Hazırlığı Binance API’den çekilen tarihsel BTC verisi pandas ile işleniyor. `open, high, low, close, volume, log_return` sütunları hazır. ### Strateji 1: SMA Crossover (Trend Takip) - Fast SMA (ör. 20 günlük) ve Slow SMA (ör. 50 günlük) - Fast > Slow → Al (pozisyon 1) - Fast < Slow → Sat (pozisyon 0) **Kritik Nokta:** `df["position"] = df["signal"].shift(1)` Kapanış fiyatıyla sinyal oluşuyor ama trade ertesi gün açılışta yapılıyor. Shift yapmazsanız **look-ahead bias** (geleceği bilme) hatası oluşur. ### Strateji 2: Momentum Son N günde pozitif getiri varsa al, negatifse çık. Matematik: `momentum = log(close[t]) - log(close[t-N])` Kısa pencere (10 gün) = daha sık trade Uzun pencere (40 gün) = daha az yanlış sinyal ### Strateji 3: Mean Reversion (Ortalamaya Dönüş) Fiyat tarihsel ortalamadan aşırı uzaklaşırsa tersine döner mantığı. Z-score ile ölçüm: `z = (price - mean) / std` - z < -1 → Al - z > 0 → Sat Bollinger Band’larla görselleştirildiğinde dipteki yeşil bölgeler alım sinyali veriyor. ### Stratejilerin Karşılaştırması (Günlük BTC Verisi) | Strateji | Piyasada Kalma Süresi | Yaklaşık Trade Sayısı | |-----------------------|-----------------------|-----------------------| | SMA Crossover (20/50) | \~U | 8-12 | | Momentum (20 gün) | \~U | 20-30 | | Mean Reversion (20/-1)| \~ | 15-25 | - Trend takip stratejileri (SMA & Momentum) uzun trendlerde güçlü - Mean Reversion yan piyasa (sideways) koşullarında etkili ### strategies.py Modülü Tüm stratejiler tek bir temiz modülde toplandı. Sonraki bölümlerde direkt import edip backtest yapacağız. **Pratik Ödevleri:** - SMA’da farklı periyotlar deneyin (10/30, 50/200) - Momentum’a %5 eşik ekleyin - Mean Reversion’da z-score’u -0.5, -1.5, -2.0 olarak değiştirin - Aynı stratejileri ETH/USDT üzerinde test edin **Özet:** Bu üç strateji algoritmik trading’in ABC’sidir. Temiz kod, doğru position shift ve piyasa rejimlerine göre strateji seçimi en önemli dersler. Gerçek parayla trade etmeden önce mutlaka backtest risk yönetimi yapın. Bu seri eğitim amaçlıdır. 5. Bölümde gerçek backtest sonuçlarını göreceğiz: getiri, maksimum drawdown, Sharpe ratio ve buy-and-hold ile karşılaştırma. Siz bu üç stratejiden hangisini önce denemek istersiniz? SMA Crossover mı, Momentum mu, yoksa Mean Reversion mu? Yorumlarda belirtin, en çok merak edilen parametreleri birlikte inceleyelim! 📈 #QuantTrading #AlgoritmikTrading #PythonTrading #SMACrossover #MomentumStrategy #MeanReversion #BTCTrading #CryptoQuant #TradingBot

New role, new skills,Growing as a software developer #python #WebDev #Coding #SoftwareDevelopment #pythontrading #ForexAlgo #100DaysOfCode

🚀Built a TradingView → TopstepX Execution Bot using the ProjectX API.🚀 ✅Runs locally with ngrok. ‼️Execution only no signals. Full user control. 👉🏽Info in bio. #FuturesTrading #AlgoTrading #TradingView #TopstepX #PropTrading #PythonTrading

always be paranoid and underconfident in trading, the worst part is trading i never making less money but losing it all. #AlgoTrading #OptionsTrading #FyersAPI #PythonTrading #IndexOptions #StockMarketIndia #Nifty50 #SystemTrading #StoicStockTrader

Zerodha x QuantInsti: Algo Trading with Kite Connect in Python This 11-hour course, offered free by Quantra by QuantInsti, provides a complete walkthrough across 22 sections and over 200 units. It guides you from the initial introduction to successfully running codes on your local machine. We've included five detailed videos, each of which is a complete unit from the full course. For full access to the complete course, including notebooks and quizzes, please visit: quantra.quantinsti.com/cours… Today’s video. Part 1. Introduction to Automated Trading and Zerodha’s Ecosystem For traders who ask: •How do I convert a discretionary idea into a rule-based strategy •What changes when I move from manual orders to API orders •Where does Kite Connect fit in my trading stack This video covers: •Why automation helps remove execution delays and emotional decisions •How a simple moving average crossover can be expressed as rules and later coded •The high-level architecture you will build in later videos. Data, logic, risk layer, and execution using Kite Connect About this free course on Quantra by QuantInsti Automate trading with Zerodha’s Kite Connect API using Python. In this hands-on course, you will build, test, and deploy a complete algorithmic trading system, from fetching live and historical market data and placing advanced orders to streaming data with WebSockets. Execute strategies, manage risk, and handle special order types like GTT and Iceberg. With practical notebooks, quizzes, and real-world examples, gain the skills to create scalable, rule-based trading systems like a pro. Free course: quantra.quantinsti.com/cours… Authors Zerodha @zerodha was founded on August 15, 2010, to break down the barriers Indian traders and investors face in cost, support, and technology. The name combines Zero with Rodha, the Sanskrit word for barrier. Today, Zerodha is India’s largest stockbroker, with over 1.6 crore clients contributing over 15 percent of all retail trading volumes. Zerodha’s developer friendly APIs and robust technology infrastructure have made algorithmic trading accessible to retail traders across India. The Kite Connect API powers thousands of trading algorithms, enabling traders to automate strategies and execute orders programmatically at scale. QuantInsti @QuantInsti is the world's leading algorithmic and quantitative trading research and training institute with registered users in 190 plus countries and territories. An initiative by the founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning and financial applications-based ecosystem for more than 10 years. #AlgorithmicTrading #KiteConnect #PythonTrading #Zerodha #Quantra

You’ll know you’ve matured when missing a trade feels better than forcing one. #AlgoTrading #OptionsTrading #FyersAPI #PythonTrading #IndexOptions #StockMarketIndia #Nifty50 #SystemTrading #StoicStockTrader

Most traders still click “Buy” and “Sell.” The ones building bots are clicking less — and thinking smarter. If you’re still manually trading your setups… someone else already coded them. Automation helps you better control and define your logic. The API handles the execution. The future of trading is automated → public.com/api #AlgoTrading #PythonTrading #TradingBots #BuildInPublic #APIs

a test of one of the strategies written without any manual code writing - only through proper meta-prompting. thanks to the open API from @binance , as well as @github (copilot) powered by Grok Fast Code 1 by @xai . #MetaPrompting #AlgorithmicTrading #PythonTrading #AIAutomation #BinanceAPI #GitHubCopilot #xAi #QuantResearch #NoCode #TradingBots

🎯 Destek & Direnç Seviyelerini Otomatik Belirlemenin 7 Yolu (Python) Piyasada en çok konuşulan kavramlardan biri: destek ve direnç seviyeleri. Yükselişi durduran “duvarlar”, düşüşü yavaşlatan “zeminler”… Ama bu seviyeleri manuel çizmeye çalışmak çoğu zaman subjektif olur. Oysa artık Python ile bu seviyeleri algoritmik olarak bulmak mümkün 🤖📈 Bugün sizinle, destek ve direnç belirlemede kullanılan 7 farklı yöntemi paylaşıyorum 👇 1️⃣ Rolling Midpoint Range – Fiyatın belirli bir periyottaki yüksek ve düşük ortalamasıyla bölge oluşturur. 2️⃣ Fibonacci Retracement – Fiyatın yüzde 23,6–61,8 arasındaki geri çekilmelerinde potansiyel dönüş seviyelerini yakalar. 3️⃣ Swing Highs & Lows – Fiyatın geçmişte dönüm yaptığı zirve ve dip noktaları baz alınır. 4️⃣ Pivot Point Analizi – Günlük/haftalık işlem aralıklarından hesaplanır, özellikle gün içi traderlar için idealdir. 5️⃣ K-Means Fiyat Kümelemesi – Sık ziyaret edilen fiyat bölgelerini istatistiksel olarak belirler. 6️⃣ Volume Profiler – Fiyatla birlikte hacmi analiz ederek, alım-satımın yoğunlaştığı seviyeleri gösterir. 7️⃣ Regresyon Modelleri (Doğrusal & Polinom) – Fiyat hareketlerini matematiksel olarak tahmin eder, eğilim çizgilerini dinamik hale getirir. 💡 Neden önemli? Çünkü bu yöntemlerle destek–direnç çizmek artık “göz kararı” değil, veri temelli bir karar haline geliyor. Böylece hatalı girişlerden kaçınıp daha isabetli pozisyonlar alınabiliyor. 📊 Bu sistemleri Python ile nasıl uygulayabileceğini, kod örnekleriyle anlattım. Detaylar için 🔗: drive.google.com/drive/u/1/f… #Borsa #PythonTrading #AlgoTrading #kripto #ALTIN #ALTINS1

The more indicators on your chart, the less conviction in your plan. #AlgoTrading #OptionsTrading #FyersAPI #PythonTrading #IndexOptions #StockMarketIndia #Nifty50 #SystemTrading #StoicStockTrader

Many options traders are familiar with volatility skew, yet it often remains a puzzle. However, approaching it from a delta-neutral angle can help you see the market in a way that brings subtle shifts into focus. But what is delta-neutral volatility skew, and how can you create a trading strategy around it? Read the Full Case Study Here: Case Study: Delta-Neutral Skew Trading: quantra.quantinsti.com/cours… - What you’ll learn in this case study and hands-on lab: - What volatility skew is and why it exists - Why a delta-neutral approach clarifies the signal - How to calculate delta-neutral skew step by step - How to translate skew into trades and risk controls You can also try it in Python NOtebook: (Login Required) quantra.quantinsti.com/start… Interested in a deep dive? Go beyond Greeks and master advanced options volatility concepts through hands-on Python labs and real case studies. Learn to measure and trade skew, calculate IV skew, IV rank, and skew rank, and develop delta-neutral portfolios. Build and backtest strategies like straddles and calendar spreads, apply machine learning for entry and exit rules, and manage portfolio risk effectively using Greeks such as delta and gamma. Enroll now in Advanced Options Volatility: Delta-Neutral Skew & Portfolio Hedging quantra.quantinsti.com/cours… #OptionsTrading #VolatilitySkew #DeltaNeutral #ImpliedVolatility #QuantFinance #PythonTrading #RiskManagement #MarketMicrostructure

🧵 Python code to find Buffett-style Indian stocks Look for: Nestle India, Colgate-Palmolive, P&G Hygiene #PythonTrading #ValueInvesting

If you trade to feel alive, you’ll end up broke. If you trade like it’s boring, you’ll end up free. #AlgoTrading #OptionsTrading #FyersAPI #PythonTrading #IndexOptions #StockMarketIndia #Nifty50 #SystemTrading #StoicStockTrader

Emotions are amplified in option selling. Automation isn’t optional—it’s survival insurance. #AlgoTrading #OptionsTrading #FyersAPI #PythonTrading #IndexOptions #StockMarketIndia #Nifty50 #SystemTrading #StoicStockTrader

The market rewards patience and punishes impatience. Short gamma amplifies both. Learn to sit through discomfort. #AlgoTrading #OptionsTrading #FyersAPI #PythonTrading #IndexOptions #StockMarketIndia #Nifty50 #SystemTrading #StoicStockTrader

Most traders trade the signal. Smart traders trade the context. A breakout isn’t enough; what happens after matters more. #AlgoTrading #OptionsTrading #FyersAPI #PythonTrading #IndexOptions #StockMarketIndia #Nifty50 #SystemTrading #StoicStockTrader