pyquotex is a Python library designed to easily integrate with the Quotex API, enabling automated trading operations. Fully open-source and licensed under MIT, the library provides features like order execution, balance checking, real-time market data collection, and more. Perfect for traders and developers looking to build efficient and customized solutions.
The PyQuotex library provides a comprehensive implementation of the most commonly used technical indicators in trading. Each indicator can be calculated across different timeframes and monitored in real-time.
The following timeframes are available for all indicators:
valid_timeframes = {
60: "1 minute",
300: "5 minutes",
900: "15 minutes",
1800: "30 minutes",
3600: "1 hour",
7200: "2 hours",
14400: "4 hours",
86400: "1 day"
}
All indicators return a similar structure that includes:
{
"indicator_values": [...], # List of historical values
"current": value, # Current value of the indicator
"timeframe": timeframe, # Timeframe used
"timestamps": [...], # List of corresponding timestamps
"history_size": size # Number of historical values
}
The Relative Strength Index measures the speed and magnitude of price movements.
# Basic calculation
rsi = await client.calculate_indicator(
asset="EURUSD",
indicator="RSI",
params={"period": 14},
timeframe=300 # 5 minutes
)
# Response structure
{
"rsi": [values...],
"current": 65.45,
"history_size": 100,
"timeframe": 300,
"timestamps": [timestamps...]
}
The MACD is a trend-following indicator that shows the relationship between two moving averages.
# Basic calculation
macd = await client.calculate_indicator(
asset="EURUSD",
indicator="MACD",
params={
"fast_period": 12,
"slow_period": 26,
"signal_period": 9
},
timeframe=900 # 15 minutes
)
# Response structure
{
"macd": [values...],
"signal": [values...],
"histogram": [values...],
"current": {
"macd": 0.00125,
"signal": 0.00100,
"histogram": 0.00025
},
"timeframe": 900,
"timestamps": [timestamps...]
}
Bollinger Bands are a volatility indicator that creates upper and lower bands around the price.
# Basic calculation
bollinger = await client.calculate_indicator(
asset="EURUSD",
indicator="BOLLINGER",
params={
"period": 20,
"std": 2
},
timeframe=1800 # 30 minutes
)
# Response structure
{
"upper": [values...],
"middle": [values...],
"lower": [values...],
"current": {
"upper": 1.1050,
"middle": 1.1000,
"lower": 1.0950
},
"timeframe": 1800,
"timestamps": [timestamps...]
}
The Stochastic Oscillator is a momentum indicator that compares the closing price with the price range over a period.
# Basic calculation
stochastic = await client.calculate_indicator(
asset="EURUSD",
indicator="STOCHASTIC",
params={
"k_period": 14,
"d_period": 3
},
timeframe=3600 # 1 hour
)
# Response structure
{
"k": [values...],
"d": [values...],
"current": {
"k": 75.5,
"d": 72.3
},
"timeframe": 3600,
"timestamps": [timestamps...]
}
The Average Directional Index measures the strength of a trend.
# Basic calculation
adx = await client.calculate_indicator(
asset="EURUSD",
indicator="ADX",
params={"period": 14},
timeframe=7200 # 2 hours
)
# Response structure
{
"adx": [values...],
"plus_di": [values...],
"minus_di": [values...],
"current": {
"adx": 25.5,
"plus_di": 30.2,
"minus_di": 20.1
},
"timeframe": 7200,
"timestamps": [timestamps...]
}
The Average True Range measures market volatility.
# Basic calculation
atr = await client.calculate_indicator(
asset="EURUSD",
indicator="ATR",
params={"period": 14},
timeframe=14400 # 4 hours
)
# Response structure
{
"atr": [values...],
"current": 0.00123,
"history_size": 100,
"timeframe": 14400,
"timestamps": [timestamps...]
}
Moving averages smooth price data to form a trend indicator.
# SMA (Simple Moving Average)
sma = await client.calculate_indicator(
asset="EURUSD",
indicator="SMA",
params={"period": 20},
timeframe=86400 # 1 day
)
# EMA (Exponential Moving Average)
ema = await client.calculate_indicator(
asset="EURUSD",
indicator="EMA",
params={"period": 20},
timeframe=86400 # 1 day
)
# Response structure (same for both)
{
"sma": [values...], # or "ema" for EMA
"current": 1.1000,
"history_size": 100,
"timeframe": 86400,
"timestamps": [timestamps...]
}
The Ichimoku Cloud is an indicator that shows multiple levels of support and resistance.
# Basic calculation
ichimoku = await client.calculate_indicator(
asset="EURUSD",
indicator="ICHIMOKU",
params={
"tenkan_period": 9,
"kijun_period": 26,
"senkou_b_period": 52
},
timeframe=3600 # 1 hour
)
# Response structure
{
"tenkan": [values...],
"kijun": [values...],
"senkou_a": [values...],
"senkou_b": [values...],
"chikou": [values...],
"current": {
"tenkan": 1.1000,
"kijun": 1.0990,
"senkou_a": 1.1010,
"senkou_b": 1.0980,
"chikou": 1.0995
},
"timeframe": 3600,
"timestamps": [timestamps...]
}
All indicators can be monitored in real-time using the subscribe_indicator function:
async def on_indicator_update(data):
print(f"Time: {data['time']}")
print(f"Current value: {data['value']}")
print(f"Historical values: {data['all_values']}")
# Subscribe to RSI updates
await client.subscribe_indicator(
asset="EURUSD",
indicator="RSI",
params={"period": 14},
callback=on_indicator_update,
timeframe=300 # 5 minutes
)
async def analyze_multi_timeframe():
# Analyze RSI across multiple timeframes
timeframes = [300, 900, 3600] # 5m, 15m, 1h
for tf in timeframes:
rsi = await client.calculate_indicator(
asset="EURUSD",
indicator="RSI",
params={"period": 14},
timeframe=tf
)
print(f"RSI at {tf} seconds: {rsi['current']}")
async def analyze_trend():
# Get multiple indicators
macd = await client.calculate_indicator(
asset="EURUSD",
indicator="MACD",
timeframe=3600
)
adx = await client.calculate_indicator(
asset="EURUSD",
indicator="ADX",
timeframe=3600
)
bb = await client.calculate_indicator(
asset="EURUSD",
indicator="BOLLINGER",
timeframe=3600
)
# Analyze trend
trend = {
"macd_trend": "BULLISH" if macd["current"]["histogram"] > 0 else "BEARISH",
"adx_strength": "STRONG" if adx["current"]["adx"] > 25 else "WEAK",
"volatility": bb["current"]["upper"] - bb["current"]["lower"]
}
print("Trend analysis:", trend)
async def monitor_multiple_indicators():
async def on_update(data):
print(f"Indicator: {data['indicator']}")
print(f"Current value: {data['value']}")
print(f"Timeframe: {data['timeframe']}")
print("---")
# Monitor RSI and MACD simultaneously
await asyncio.gather(
client.subscribe_indicator("EURUSD", "RSI", callback=on_update, timeframe=300),
client.subscribe_indicator("EURUSD", "MACD", callback=on_update, timeframe=300)
)