561 posts tagged with "TradingView"

Average Day Range (ADR) is an average of daily high-minus-low ranges. It provides historical range context, not a prediction of the next session's direction or maximum move. The data interval matters: averaging five-minute bar ranges is not the same as averaging daily ranges.

Converting MQL4 to MQL5 means preserving a program's intended behavior on MetaTrader 5, not merely renaming functions until it compiles. The main migration risks are data timing, indicator access and the relationship between orders, deals and positions.

Start with a concrete reason to migrate, such as a broker or platform requirement. This guide does not establish a universal MT4 shutdown deadline or a fixed speed advantage for converted programs.

The Awesome Oscillator (AO) subtracts a 34-bar simple average of midpoint prices from a 5-bar average. It describes the difference between two historical price averages. Its sign and its bar color answer different questions, and neither guarantees a future price move.

An indicator displays calculations or signals. To measure trading performance, you must add explicit entry, exit, sizing and execution rules. TradingView supports manual historical inspection through Bar Replay and automated simulation through Pine strategies in Strategy Tester. Those workflows answer different questions.

Backtesting.py is an open-source Python framework for simulating trading rules on historical data. It is useful for inspecting a single instrument's signals and trades. The examples below demonstrate its API; they do not establish that a moving-average crossover is profitable or suitable for live trading.

Backtesting.py and Backtrader run Python simulations; Pineify helps generate Pine Script for TradingView. The useful choice depends on the data, portfolio model and development workflow you need. No controlled performance benchmark or first-hand AAPL comparison is established here.

A backtesting framework should be evaluated against the strategy's execution requirements. Backtesting.py offers a Python simulation API; Backtrader provides an event-driven engine with multiple feeds and broker models; Pineify generates Pine Script and supports TradingView research workflows. Pineify is not a Pine-to-Python portfolio execution bridge.

Backtesting applies explicit trading rules to historical data under a defined execution model. It can expose weak assumptions and show how a rule behaved in a sample. It cannot certify a future edge or guarantee that live losses will remain within historical drawdowns.

A momentum squeeze indicator combines a volatility-compression condition with a directional oscillator. Compression describes the recent price distribution; it does not establish when a breakout will occur, how far it will travel or which direction will succeed. The exact script matters because TTM Squeeze, LazyBear's derivative and Pineify's BB Squeeze use different calculations.

Backtrader can connect to Alpaca through the separate alpaca-backtrader-api adapter. Historical simulation, Alpaca paper execution and live brokerage execution are distinct modes. Changing a paper flag alone does not validate credentials, install the correct broker in Cerebro or confirm compatible data feeds.