GitHub - kshlgrg/pythonpine: Pine Script-style indicator library in Python using MetaTrader5 OHLCV data — 100+ real-time indicators for algorithmic trading.

🧠 PYTHONPINE

An ultra-powerful Python library to compute over 100+ TradingView-style technical indicators using real-time OHLCV data from MetaTrader5.

📦 Think of this as Pine Script in Python — built to help algorithmic traders, quants, and curious developers power their backtests, trading bots, and research with advanced indicator logic.

🔧 Features

✅ 100+ technical indicators categorized into:

🟩 Trend Indicators
🟦 Momentum Indicators
🟧 Volatility Indicators
🟨 Volume-Based Indicators
🟪 Price Action & Support/Resistance
🟥 Oscillators & Cycles
⚫ Custom Composites
🟤 Time/Session-Based
🧠 Statistical / Nonlinear

✅ Real-time OHLCV updates from MetaTrader5
✅ Designed for clean usage — import pythonpine and start
✅ Fully extensible — easily add your own indicators
✅ Minimal setup, maximum power

🚀 Quick Start

1. Install MetaTrader5 module

pip install MetaTrader5 numpy pandas scipy scikit-learn

2. Clone this repo

git clone https://github.com/kshgrg/pythonpine
cd pythonpine

3. Add the folder to your project or install locally

pip install -e .

🛠️ How to Use

✅ Step 1: Connect to MetaTrader5

from pythonpine import *

connect_to_mt5(
    login=123456,
    password="yourpassword",
    server="yourserver",
    path="C:\\Path\\To\\Terminal64.exe"
)

✅ Step 2: Get OHLCV arrays

open, high, low, close, volume = get_ohlcv_arrays("EURUSD")

✅ Step 3: Run background price updater (Optional for real-time trading)

Paste this in your main code to auto-update price arrays every minute:

import time

while True:
    open, high, low, close, volume = get_ohlcv_arrays("EURUSD")
    time.sleep(60)  # Updates every 60 seconds

📊 Example Usage (Indicator Calculations)

# EMA and RSI
ema_20 = ema(close, length=20)
rsi_14 = rsi(close, length=14)

# Bollinger Bands
upper, middle, lower = bollinger_bands(close, length=20, std_dev=2)

# MACD
macd_line, signal_line, histogram = macd(close)

# SuperTrend
supertrend, direction = supertrend_calc(high, low, close, atr_period=10, multiplier=3)

🧪 Full Indicator List

Expand to see all indicators:

Click to expand

🟩 Trend Indicators

EMA, SMA, DEMA, TEMA, WMA, HMA, VWMA, KAMA, SuperTrend, Vortex, Aroon, Linear Regression, Donchian, FAMA, MA Envelope

🟦 Momentum Indicators

RSI, Stoch RSI, Stochastic, MACD, ROC, CCI, TRIX, Ultimate Osc, Williams %R, DMI/ADX, Elder Impulse, Schaff, CMO, RVI

🟧 Volatility Indicators

ATR, Bollinger Bands, Keltner Channel, Donchian Width, True Range, Std Dev, Chaikin Vol, Boll %B, Hist Volatility

🟨 Volume-Based Indicators

OBV, VWAP, Accum/Dist, CMF, Vol Osc, Force Index, MFI, Ease of Move, Vol ROC, Vol Delta, Intraday Intensity

🟪 Price Action / Support-Resistance

Pivot Points, Price ROC, ZigZag, Heikin Ashi, Renko, Engulfing, Pin Bar, Double Top, S/R Zones, Pattern Count

🟥 Oscillators & Cycles

Fisher Transform, Hilbert Transforms, Ehler Trendline, DPO, Laguerre RSI, QStick, SMI, Adaptive Cycle, Inverse Fisher

⚫ Meta-Indicators

MA Crossover Count, Consensus Score, Momentum-Vol Composite, Trend Strength, MACD Angle, RSI Divergence, MTF EMA

🟤 Time/Session Based

Time of Day, Session High/Low, Market Sessions Overlay, Day of Week, Time Since High/Low

🧠 Statistical/Experimental

Z-Score, Rolling Stats, Skewness, Percentile, MAD, Fractal Dim, Garman-Klass, Kalman, Hurst, Entropy, TSF, Neural Score

📘 Built-in Functions Reference

All functions are available directly after importing pythonpine. The functions are grouped by category and support standard Python lists or NumPy arrays. Make sure to call get_ohlcv_arrays() to retrieve updated price data.

🔧 Utility Functions

Function	Inputs	Description
`initialize_data()`	`source: str`, `symbol: str`, `timeframe: str`, `count: int`	Initializes global OHLCV arrays from selected data source (MetaTrader or cTrader).
`open(i=0)`	`i: int`	Returns the open price at index `i`.
`high(i=0)`	`i: int`	Returns the high price at index `i`.
`low(i=0)`	`i: int`	Returns the low price at index `i`.
`close(i=0)`	`i: int`	Returns the close price at index `i`.
`volume(i=0)`	`i: int`	Returns the volume at index `i`.
`openList(start, end)`	`start: int`, `end: int`	Returns open prices from index `start` to `end` (inclusive).
`highList(start, end)`	`start: int`, `end: int`	Returns high prices from index `start` to `end` (inclusive).
`lowList(start, end)`	`start: int`, `end: int`	Returns low prices from index `start` to `end` (inclusive).
`closeList(start, end)`	`start: int`, `end: int`	Returns close prices from index `start` to `end` (inclusive).
`volumeList(start, end)`	`start: int`, `end: int`	Returns volume values from index `start` to `end` (inclusive).
`highest(end)`	`end: int`	Returns highest high from index `0` to `end - 1`.
`lowest(end)`	`end: int`	Returns lowest low from index `0` to `end - 1`.

📈 Trend Indicators

Function Name	Inputs	Description	Tier
`ema(length)`	`length: int`	Exponential Moving Average.	✅ Core
`sma(length)`	`length: int`	Simple Moving Average.	✅ Core
`dema(length)`	`length: int`	Double Exponential MA.	✅ Core
`tema(length)`	`length: int`	Triple Exponential MA.	✅ Core
`wma(length)`	`length: int`	Weighted MA.	✅ Core
`hma(length)`	`length: int`	Hull Moving Average.	✅ Core
`vwma(length)`	`length: int`	Volume Weighted MA.	✅ Core
`kama(length)`	`length: int`	Kaufman Adaptive MA.	✅ Core
`supertrend(period, multiplier)`	`int`, `float`	ATR-based trend-following indicator.	✅ Core
`vortex(period)`	`period: int`	Trend strength via VI+ / VI−.	✅ Core
`aroon(period)`	`period: int`	Measures time since highs/lows.	✅ Core
`linear_regression(length)`	`length: int`	Line of best fit slope.	✅ Core
`donchian_channel(length)`	`length: int`	Highest high, lowest low channel.	✅ Core
`fractal_ma(length)`	`length: int`	Fractal Adaptive MA.	✅ Core
`ma_envelope(length, deviation)`	`int`, `float`	MA bands with % deviation.	✅ Core
`tillson_t3(length)`	`length: int`	Smooth advanced moving average.	🟩 Recommended
`ichimoku_cloud()`	(none)	Full Ichimoku components.	🟩 Recommended
`parabolic_sar(step, max_step)`	`float`, `float`	Trend following system with trailing stops.	🟩 Recommended
`adaptive_moving_average()`	(none)	Dynamically adjusts MA based on volatility.	🟩 Recommended
`polarized_fractal_efficiency()`	(none)	Measures trend vs. noise.	🟨 Optional
`trend_strength_indicator()`	(none)	Flags trend confidence using multiple inputs.	🟨 Optional
`dynamic_zone_ma()`	(none)	Adaptive band-based MA using zones.	🟨 Optional

🟦 Momentum Indicators

Function Name	Inputs	Description	Tier
`rsi(length)`	`length: int`	Relative Strength Index measuring gain/loss momentum.	✅ Core
`stochastic_rsi(length)`	`length: int`	RSI applied to normalized RSI values.	✅ Core
`stochastic_oscillator(k, d)`	`k: int`, `d: int`	%K and %D momentum oscillator.	✅ Core
`macd(fast, slow, signal)`	`int`, `int`, `int`	Moving Average Convergence Divergence.	✅ Core
`roc(length)`	`length: int`	Rate of Change = % price change over time.	✅ Core
`cci(length)`	`length: int`	Commodity Channel Index.	✅ Core
`trix(length)`	`length: int`	Triple-smoothed EMA momentum.	✅ Core
`ultimate_oscillator()`	(none)	Combines 3 different timeframes of momentum.	✅ Core
`williams_r(length)`	`length: int`	Measures overbought/oversold based on high-low range.	🟩 Recommended
`dmi_adx(length)`	`length: int`	Directional Movement Index and ADX.	🟩 Recommended
`momentum(length)`	`length: int`	Raw momentum = current − previous price.	🟩 Recommended
`elder_impulse()`	(none)	Combines EMA trend and MACD momentum for entry/exit.	🟩 Recommended
`schaff_trend_cycle()`	(none)	Combines MACD and cycles for fast momentum turns.	🟨 Optional
`chande_momentum_oscillator(length)`	`length: int`	CMO = (SumUp - SumDown) / (SumUp + SumDown)	🟨 Optional
`rvi(length)`	`length: int`	Relative Vigor Index: Close–Open vs High–Low.	🟨 Optional

🟧 Volatility Indicators

Function Name	Inputs	Description	Tier
`atr(length)`	`length: int`	Average True Range.	✅ Core
`bollinger_bands(length, stddev)`	`int`, `float`	MA ± standard deviation bands.	✅ Core
`keltner_channel(length, multiplier)`	`int`, `float`	EMA + ATR envelope.	✅ Core
`donchian_channel_width(length)`	`length: int`	Range width = High − Low.	✅ Core
`true_range()`	(none)	Current bar's TR (High-Low vs. PrevClose).	✅ Core
`std_dev(length)`	`length: int`	Rolling standard deviation.	✅ Core
`chaikin_volatility(length)`	`length: int`	EMA of high-low range vs. longer EMA.	✅ Core
`bollinger_percent_b()`	`length: int`	Where price is relative to Bollinger Bands.	✅ Core
`historical_volatility(length)`	`length: int`	Std deviation of log returns.	✅ Core
`average_range_percent(length)`	`length: int`	ATR as % of close price.	🟩 Recommended
`normalized_volatility(length)`	`length: int`	ATR normalized by price.	🟩 Recommended
`range_ratio_indicator(length)`	`length: int`	Ratio of candle ranges.	🟩 Recommended
`rolling_volatility_spikes()`	(none)	Flags bars with unusually high volatility.	🟩 Recommended
`ulcer_index(length)`	`length: int`	Drawdown-based risk metric.	🟨 Optional
`volatility_stop()`	`length: int`	Volatility-based trailing stop logic.	🟨 Optional
`atr_trailing_band()`	`length: int`	Custom trailing stop bands via ATR.	🟨 Optional

🟨 Volume-Based Indicators

Function Name	Inputs	Description	Tier
`obv()`	(none)	On Balance Volume.	✅ Core
`vwap()`	(none)	Volume Weighted Average Price.	✅ Core
`ad_line()`	(none)	Accumulation/Distribution Line.	✅ Core
`cmf(length)`	`length: int`	Chaikin Money Flow.	✅ Core
`volume_oscillator(short, long)`	`int`, `int`	Volume short MA − long MA.	✅ Core
`force_index(length)`	`length: int`	Volume × Price Change.	✅ Core
`mfi(length)`	`length: int`	Money Flow Index.	✅ Core
`eom(length)`	`length: int`	Ease of Movement (Price-Volume flow).	✅ Core
`vroc(length)`	`length: int`	Volume Rate of Change.	✅ Core
`volume_delta()`	(none)	Buy volume − Sell volume estimate.	🟩 Recommended
`intraday_intensity(length)`	`length: int`	Closing location + volume weighted.	🟩 Recommended
`balanced_volume_flow(length)`	`length: int`	Refined version of OBV with thresholds.	🟩 Recommended
`volume_climax_spike()`	(none)	Flags high-volume candles.	🟩 Recommended
`relative_volume(length)`	`length: int`	Volume vs. recent average.	🟨 Optional
`smoothed_volume_ratio()`	(none)	Custom EMA of volume divided by baseline.	🟨 Optional
`vfi(length)`	`length: int`	Volume Flow Index.	🟨 Optional

🟪 Price Action / Support & Resistance

Function Name	Inputs	Description	Tier
`pivot_points(type, period)`	`type: str`, `period: int`	Classic, Fibonacci, Camarilla pivot levels.	✅ Core
`price_roc(period)`	`period: int`	Price Rate of Change (momentum).	✅ Core
`zigzag(threshold)`	`threshold: float`	Detects swings above a % threshold.	✅ Core
`heikin_ashi()`	(none)	Converts raw OHLC to Heikin Ashi.	✅ Core
`renko_boxes(box_size)`	`box_size: float`	Renko-style brick chart generation.	✅ Core
`detect_engulfing()`	(none)	Bullish/bearish engulfing detection.	✅ Core
`detect_pinbar()`	(none)	Detects pin bars based on wick-body ratio.	✅ Core
`double_top_bottom(period, threshold)`	`period: int`, `threshold: float`	Detects double top/bottom patterns.	✅ Core
`support_resistance_zones(sensitivity)`	`sensitivity: float`	Finds key horizontal SR zones.	✅ Core
`candlestick_pattern_count(n, pattern)`	`n: int`, `pattern: str`	Counts pattern occurrence over last `n` bars.	✅ Core
`three_bar_reversal()`	(none)	Identifies 3-bar reversals (e.g., swing setups).	🟩 Recommended
`doji_finder()`	(none)	Detects indecision candles (Doji).	🟩 Recommended
`price_gap_detector(threshold)`	`threshold: float`	Flags gap ups/downs between candles.	🟩 Recommended
`range_breakout_flags(window)`	`window: int`	Flags when price breaks from tight range.	🟩 Recommended
`range_contraction_detection(window)`	`window: int`	Detects squeeze zones (narrow price ranges).	🟨 Optional
`candle_shadow_ratio()`	(none)	Calculates body-to-wick ratio. Useful for bar classification.	🟨 Optional
`bar_color_classifier()`	(none)	Classifies candles as bullish, bearish, indecisive.	🟨 Optional

🟥 Oscillators & Cycles

Function Name	Inputs	Description	Tier
`fisher_transform(source, length, smooth, smooth_period)`	`List[float]`, `int`, `bool`, `int`	Normalized Fisher transform with smoothing.	✅ Core
`hilbert_transform(series)`	`series: List[float]`	Computes in-phase and quadrature wave components.	✅ Core
`ht_sine(series)`	`series: List[float]`	Generates sine and leadsine for cycle phase.	✅ Core
`ht_phase(series)`	`series: List[float]`	Computes instantaneous phase shift.	✅ Core
`ht_trendline(series)`	`series: List[float]`	Smooth filtered trendline from Hilbert Transform.	✅ Core
`ht_dominant_cycle(series)`	`series: List[float]`	Detects dominant price cycle length.	✅ Core
`dpo(series, period)`	`series: List[float]`, `period: int`	Detrended Price Oscillator.	✅ Core
`ehlers_instantaneous_trendline(series)`	`series: List[float]`	Ehlers’ smoothed cycle tracking trendline.	✅ Core
`phase_accumulation(series)`	`series: List[float]`	Tracks phase change to estimate cycle count.	✅ Core
`inverse_fisher(series)`	`series: List[float]`	Applies inverse Fisher transform (0–1 bounded).	✅ Core
`roofing_filter(series, period)`	`series: List[float]`, `period: int`	Removes low/high-frequency components (Ehlers filter).	🟩 Recommended
`bandpass_filter(series, low, high)`	`series: List[float]`, `float`, `float`	Filters signal to a defined band of frequencies.	🟩 Recommended
`ehlers_deviation_cycle(series)`	`series: List[float]`	Experimental variation of DPO with deviation band.	🟩 Recommended
`cycle_trigger(series, length)`	`series: List[float]`, `length: int`	Generates on/off signals based on phase alignment.	🟩 Recommended
`dominant_cycle_index(series)`	`series: List[float]`	Measures confidence in a dominant cycle.	🟨 Optional
`adaptive_cycle_filter(series)`	`series: List[float]`	Auto-tunes filtering to current market rhythm.	🟨 Optional
`wavelet_transform_swing(series)`	`series: List[float]`	Experimental: wavelet-based swing logic (placeholder).	🟨 Optional

⚫ Meta-Indicators / Custom Composites

Function Name	Inputs	Description
`ma_crossover_signal_count(fast_ma, slow_ma)`	`fast_ma`, `slow_ma`	Counts moving average crossovers.
`indicator_consensus(*indicators)`	`*indicators`	Aggregates multiple indicators for consensus score.
`momentum_volatility_composite(rsi, atr)`	`rsi`, `atr`	Combines RSI and ATR into a composite score.
`trend_strength_score(adx, ma_slope)`	`adx`, `ma_slope`	Scores trend strength using ADX and MA slope.
`macd_histogram_angle(macd_hist)`	`macd_hist`	Measures angle of change in MACD histogram.
`rsi_divergence_count(rsi, price)`	`rsi`, `price`	Detects number of RSI divergences.
`volume_spike_flag(volume)`	`volume`	Flags volume spikes relative to recent activity.
`multi_timeframe_ema_alignment(*emas)`	`*emas`	Checks EMA alignment across timeframes.
`trend_reversal_likelihood(rsi, macd)`	`rsi`, `macd`	Estimates chance of trend reversal.
`consolidation_detector(close)`	`close`	Detects price consolidation zones.

🟤 Time-Based & Session Indicators

Function Name	Inputs	Description	Tier
`time_of_day_normalized()`	(none)	Converts time to normalized 0–1 range.	✅ Core
`session_high_low()`	(none)	Tracks high/low of current session.	✅ Core
`session_overlay(region)`	`region: str`	Shows active hours (London, NY, Asia).	✅ Core
`day_of_week_encoding()`	(none)	Encodes Monday–Friday numerically.	✅ Core
`time_since_high(period)`	`period: int`	Time since last n-bar high.	✅ Core
`time_since_low(period)`	`period: int`	Time since last n-bar low.	✅ Core
`intraday_volatility_window()`	(none)	Compares volatility in different hours.	🟩 Recommended
`session_range_width()`	(none)	Tracks open-to-close distance in session.	🟩 Recommended
`hourly_volume_profile()`	(none)	Aggregates volume per hour.	🟩 Recommended
`active_hour_marker()`	(none)	Marks overlapping sessions or active ranges.	🟩 Recommended
`time_gap_detector()`	(none)	Flags missing bars or large time gaps.	🟨 Optional
`session_stdev_band()`	(none)	Rolling volatility band per session.	🟨 Optional
`session_smooth_trend()`	(none)	MA only inside market hours.	🟨 Optional

🧠 Statistical & Derived Indicators

Function Name	Inputs	Description
`z_score(close, window=20)`	`close`, `window`	Calculates z-score of price over a window.
`rolling_mean_std(close, window=20)`	`close`, `window`	Returns rolling mean and standard deviation.
`skew_kurt(close, window=20)`	`close`, `window`	Calculates skewness and kurtosis.
`percentile_rank(close, window=20)`	`close`, `window`	Finds price percentile rank.
`mad(close, window=20)`	`close`, `window`	Computes Median Absolute Deviation.

🧪 Experimental / Nonlinear Indicators

Function Name	Inputs	Description
`fractal_dimension_index(close)`	`close`	Estimates market roughness.
`garman_klass_volatility(open, high, low, close)`	`open`, `high`, `low`, `close`	Volatility estimator using Garman-Klass model.
`kalman_filter_slope(close)`	`close`	Smooths trend using Kalman filter.
`hurst_exponent(close)`	`close`	Calculates Hurst Exponent for fractal behavior.
`shannon_entropy(close, bins=10)`	`close`, `bins`	Measures entropy in price distribution.
`kld_price(close1, close2)`	`close1`, `close2`	KL divergence between price series.
`tsf(close, length=14)`	`close`, `length`	Time Series Forecast using linear regression.
`roofing_filter(close)`	`close`	Ehler’s Roofing Filter to remove high-frequency noise.
`smoothed_heikin_ashi_osc(open, high, low, close)`	`open`, `high`, `low`, `close`	Oscillator using smoothed Heikin Ashi.
`neural_indicator_score(features)`	`features`	Outputs a score from trained neural model using features.

📚 Tutorials & Help

We recommend using a Jupyter notebook or Python script to:

Import pythonpine
Connect to MetaTrader5
Pull the price arrays every minute
Pass those arrays to your desired custom indicator functions

📢 Want to Share?

If you're using this library in trading, research, or just for learning — feel free to tag or DM me on Instagram:

📸 @kushalgarggg

🛡️ License

This library is licensed under Creative Commons BY-NC-SA 4.0.

✅ Free to use for personal and educational purposes
❌ Commercial or profit-based use requires permission
🔁 Attribution and same-license distribution required

More at: LICENSE

💡 Contribution

Want to improve or expand this project? Feel free to fork, star 🌟, and submit PRs!




This project is licensed under the GNU Affero General Public License v3.0.  
See the [LICENSE](LICENSE) file for more information.

For commercial use, please contact the author.

Name		Name	Last commit message	Last commit date
Latest commit History 36 Commits
.github/workflows		.github/workflows
LICENSE		LICENSE
README.md		README.md
__init__.py		__init__.py
cycles.py		cycles.py
experimental.py		experimental.py
meta.py		meta.py
momentum.py		momentum.py
price_action.py		price_action.py
price_fetcher.py		price_fetcher.py
settings.json		settings.json
setup.py		setup.py
statistical.py		statistical.py
time-session.py		time-session.py
trend.py		trend.py
volatility.py		volatility.py
volume.py		volume.py

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🔧 Features

🚀 Quick Start

1. Install MetaTrader5 module

2. Clone this repo

3. Add the folder to your project or install locally

🛠️ How to Use

✅ Step 1: Connect to MetaTrader5

✅ Step 2: Get OHLCV arrays

✅ Step 3: Run background price updater (Optional for real-time trading)

📊 Example Usage (Indicator Calculations)

🧪 Full Indicator List

🟩 Trend Indicators

🟦 Momentum Indicators

🟧 Volatility Indicators

🟨 Volume-Based Indicators

🟪 Price Action / Support-Resistance

🟥 Oscillators & Cycles

⚫ Meta-Indicators

🟤 Time/Session Based

🧠 Statistical/Experimental

📘 Built-in Functions Reference

🔧 Utility Functions

📈 Trend Indicators

🟦 Momentum Indicators

🟧 Volatility Indicators

🟨 Volume-Based Indicators

🟪 Price Action / Support & Resistance

🟥 Oscillators & Cycles

⚫ Meta-Indicators / Custom Composites

🟤 Time-Based & Session Indicators

🧠 Statistical & Derived Indicators

🧪 Experimental / Nonlinear Indicators

📚 Tutorials & Help

📢 Want to Share?

🛡️ License

💡 Contribution

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