import ccxt
import pandas as pd
from smartmoneyconcepts import smc

exchange = ccxt.binanceusdm(
    {
        "apiKey": "1",
        "secret": "1",
        "enableRateLimit": True,
    }
)

# exchange.verbose = True
exchange.options["warnOnFetchOpenOrdersWithoutSymbol"] = False

klines = exchange.fetch_ohlcv("POLYX/USDT", "1h", limit=100)

# 转为 df
df = pd.DataFrame(
    klines, columns=["timestamp", "open", "high", "low", "close", "volume"]
)

swing_highs_lows = smc.swing_highs_lows(df, 50)
print(swing_highs_lows)

# fvg_data = smc.fvg(df)
# df = pd.concat([df, fvg_data], axis=1)

# swing_highs_lows = smc.swing_highs_lows(df)
# print(swing_highs_lows)

# print(df)

#----------------Binance Data Fecth---------------------------------
bars = exchange.fetch_ohlcv('LINK/USDT', timeframe='4h', limit=1000)
df = pd.DataFrame(bars[:-1], columns=['timestamp', 'open', 'high', 'low', 'close', 'volume']).astype(float)
df['timestamp']=pd.to_datetime(df['timestamp'], unit='ms')

#----------------Bar Plot------------------------------------------
fig = go.Figure(
    data=[
        go.Candlestick(
            x=df["timestamp"],
            open=df["open"],
            high=df["high"],
            low=df["low"],
            close=df["close"],
        )
    ]
)
#----------------FVG Plot------------------------------------------
def add_FVG(fig):
    fvg_data = smc.fvg(df)
    # plot a rectangle for each fvg
    for i in range(len(fvg_data["FVG"])):
        if fvg_data["FVG"][i] != 0:
            x1 = (
                fvg_data["MitigatedIndex"][i]
                if fvg_data["MitigatedIndex"][i] != 0
                else len(df) - 1
            )
            fig.add_shape(
                # filled Rectangle
                type="rect",
                x0=df.index[i],
                y0=fvg_data["Top"][i],
                x1=df.index[x1],
                y1=fvg_data["Bottom"][i],
                line=dict(
                    width=0,
                ),
                fillcolor="yellow",
                opacity=0.5,
            )
    return fig
#----------------Highs & Lows------------------------------------------
def add_highs_lows(fig):
    highs_lows_data = smc.highs_lows(df,0.005)

    # remove from highs_lows_data
    indexs = []
    levels = []
    for i in range(len(highs_lows_data)):
        if highs_lows_data["HighsLows"][i] != 0:
            indexs.append(i)
            levels.append(highs_lows_data["Levels"][i])
    
    # plot these lines on a graph
    for i in range(len(indexs) - 1):
        fig.add_trace(
            go.Scatter(
                x=[df["timestamp"][indexs[i]], df["timestamp"][indexs[i + 1]]],
                y=[levels[i], levels[i+1]],
                mode="lines",
                line=dict(
                    color="green" if highs_lows_data["HighsLows"][indexs[i]] == -1 else "red",
                ),
            )
        )

    return fig
#----------------Plot Liquidity------------------------------------------
def add_liquidity(fig):
    liquidity_data = smc.liquidity(df)

    # draw a line horizontally for each liquidity level
    for i in range(len(liquidity_data["Liquidity"])):
        if liquidity_data["Liquidity"][i] != 0:
            fig.add_trace(
                go.Scatter(
                    x=[df["timestamp"][i], df["timestamp"][liquidity_data["End"][i]]],
                    y=[liquidity_data["Level"][i], liquidity_data["Level"][i]],
                    mode="lines",
                    line=dict(
                        color="orange",
                    ),
                )
            )
        if liquidity_data["Swept"][i] != 0:
            # draw a red line between the end and the swept point
            fig.add_trace(
                go.Scatter(
                    x=[
                        df["timestamp"][liquidity_data["End"][i]],
                        df["timestamp"][liquidity_data["Swept"][i]],
                    ],
                    y=[
                        liquidity_data["Level"][i],
                        (
                            df["high"][liquidity_data["Swept"][i]]
                            if liquidity_data["Liquidity"][i] == 1
                            else df["low"][liquidity_data["Swept"][i]]
                        ),
                    ],
                    mode="lines",
                    line=dict(
                        color="red",
                    ),
                )
            )
    return fig

fig = add_FVG(fig)
fig = add_highs_lows(fig)
fig = add_liquidity(fig)
fig.show()

def add_VOB(fig):

    start_time = time.time()
    ob_data = smc.vob(df)
    volumized_ob_time = time.time() - start_time
    logger.debug(
        "volumized_ob processing time (for ohlc length={}): {:.2f} seconds",
        len(ob_data),
        volumized_ob_time,
    )

2024-02-23 12:48:40.440 | DEBUG    | __main__:add_VOB:194 - volumized_ob processing time (for ohlc length=1000): 5.01 seconds