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import gradio as gr
import pandas as pd
import requests
from prophet import Prophet
import plotly.graph_objs as go
import math
import numpy as np
# Constants for API endpoints
OKX_TICKERS_ENDPOINT = "https://www.okx.com/api/v5/market/tickers?instType=SPOT"
OKX_CANDLE_ENDPOINT = "https://www.okx.com/api/v5/market/candles"
TIMEFRAME_MAPPING = {
"1m": "1m",
"5m": "5m",
"15m": "15m",
"30m": "30m",
"1h": "1H",
"2h": "2H",
"4h": "4H",
"6h": "6H",
"12h": "12H",
"1d": "1D",
"1w": "1W",
}
# Function to calculate technical indicators
def calculate_technical_indicators(df):
# RSI Calculation
delta = df['close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
rs = gain / loss
df['RSI'] = 100 - (100 / (1 + rs))
# MACD Calculation
exp1 = df['close'].ewm(span=12, adjust=False).mean()
exp2 = df['close'].ewm(span=26, adjust=False).mean()
df['MACD'] = exp1 - exp2
df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()
# Bollinger Bands Calculation
df['MA20'] = df['close'].rolling(window=20).mean()
df['BB_upper'] = df['MA20'] + 2 * df['close'].rolling(window=20).std()
df['BB_lower'] = df['MA20'] - 2 * df['close'].rolling(window=20).std()
return df
# Function to create technical analysis charts
def create_technical_charts(df):
# Price and Bollinger Bands Chart
fig1 = go.Figure()
fig1.add_trace(go.Candlestick(
x=df['timestamp'],
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close'],
name='Price'
))
fig1.add_trace(go.Scatter(x=df['timestamp'], y=df['BB_upper'], name='Upper BB', line=dict(color='gray', dash='dash')))
fig1.add_trace(go.Scatter(x=df['timestamp'], y=df['BB_lower'], name='Lower BB', line=dict(color='gray', dash='dash')))
fig1.update_layout(title='Price and Bollinger Bands', xaxis_title='Date', yaxis_title='Price')
# RSI Chart
fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=df['timestamp'], y=df['RSI'], name='RSI'))
fig2.add_hline(y=70, line_dash="dash", line_color="red")
fig2.add_hline(y=30, line_dash="dash", line_color="green")
fig2.update_layout(title='RSI Indicator', xaxis_title='Date', yaxis_title='RSI')
# MACD Chart
fig3 = go.Figure()
fig3.add_trace(go.Scatter(x=df['timestamp'], y=df['MACD'], name='MACD'))
fig3.add_trace(go.Scatter(x=df['timestamp'], y=df['Signal_Line'], name='Signal Line'))
fig3.update_layout(title='MACD', xaxis_title='Date', yaxis_title='Value')
return fig1, fig2, fig3
# Fetch available symbols from OKX API
def fetch_okx_symbols():
try:
resp = requests.get(OKX_TICKERS_ENDPOINT)
data = resp.json().get("data", [])
symbols = [item["instId"] for item in data if item.get("instType") == "SPOT"]
return ["BTC-USDT"] + symbols if symbols else ["BTC-USDT"]
except Exception as e:
return ["BTC-USDT"]
# Fetch historical candle data from OKX API
def fetch_okx_candles(symbol, timeframe="1H", total=2000):
calls_needed = math.ceil(total / 300)
all_data = []
for _ in range(calls_needed):
params = {"instId": symbol, "bar": timeframe, "limit": 300}
resp = requests.get(OKX_CANDLE_ENDPOINT, params=params)
data = resp.json().get("data", [])
if not data:
break
columns = ["ts", "o", "h", "l", "c"]
df_chunk = pd.DataFrame(data, columns=columns)
df_chunk.rename(columns={"ts": "timestamp", "o": "open",
"h": "high", "l": "low",
"c": "close"}, inplace=True)
all_data.append(df_chunk)
if len(data) < 300:
break
if not all_data:
return pd.DataFrame()
df_all = pd.concat(all_data)
# Convert timestamps to datetime and calculate indicators
...
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