Delete app.py

app.py

@@ -1,115 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-
-TITLE = "AF Global Expert — Python Terminal"
-DISCLAIMER = (
-    "Research/backtest terminal only. This is a Python reconstruction scaffold, not a "
-    "verified 1:1 port of the original MT4 Expert Advisor. It does not place live orders."
-)
-
-def ema(s, n):
-    return s.ewm(span=int(n), adjust=False).mean()
-
-def rsi(close, n=9):
-    d = close.diff()
-    gain = d.clip(lower=0).ewm(alpha=1/n, adjust=False).mean()
-    loss = (-d.clip(upper=0)).ewm(alpha=1/n, adjust=False).mean()
-    rs = gain / loss.replace(0, np.nan)
-    return 100 - 100/(1+rs)
-
-def macd(close, fast=8, slow=17, signal=9):
-    line = ema(close, fast) - ema(close, slow)
-    sig = ema(line, signal)
-    return line, sig
-
-def analyze(file, symbol, lot, lot_exp, tp_pips, pip_size, macd_fast, macd_slow,
-            macd_signal, rsi_period, cci_period, stoch_k, stoch_d):
-    if file is None:
-        return "Upload an OHLC CSV file to begin.", None
-    try:
-        df = pd.read_csv(file.name)
-        df.columns = [str(c).strip().lower() for c in df.columns]
-        aliases = {"date":"time", "datetime":"time", "timestamp":"time",
-                   "open":"open", "high":"high", "low":"low", "close":"close",
-                   "volume":"volume", "tick_volume":"volume"}
-        df = df.rename(columns={c: aliases[c] for c in df.columns if c in aliases})
-        required = {"open","high","low","close"}
-        if not required.issubset(df.columns):
-            return "CSV must contain open, high, low, close columns.", None
-        for c in required:
-            df[c] = pd.to_numeric(df[c], errors="coerce")
-        df = df.dropna(subset=list(required)).reset_index(drop=True)
-        if len(df) < max(macd_slow, cci_period, stoch_k) + 5:
-            return "Not enough candles for the selected indicator periods.", None
-
-        close, high, low = df.close, df.high, df.low
-        m, ms = macd(close, macd_fast, macd_slow, macd_signal)
-        df["MACD"] = m
-        df["MACD_signal"] = ms
-        df["RSI"] = rsi(close, rsi_period)
-        tp = (high + low + close) / 3
-        mean = tp.rolling(cci_period).mean()
-        mad = tp.rolling(cci_period).apply(lambda x: np.mean(np.abs(x - np.mean(x))), raw=True)
-        df["CCI"] = (tp - mean) / (0.015 * mad.replace(0, np.nan))
-        ll = low.rolling(stoch_k).min()
-        hh = high.rolling(stoch_k).max()
-        df["Stoch_K"] = 100 * (close - ll) / (hh - ll).replace(0, np.nan)
-        df["Stoch_D"] = df.Stoch_K.rolling(stoch_d).mean()
-        df["EMA_5"] = ema(close, 5)
-        df["EMA_9"] = ema(close, 9)
-
-        # Transparent indicator confluence, not the EA's proprietary entry engine.
-        bullish = (df.MACD > df.MACD_signal) & (df.RSI > 50) & (df.EMA_5 > df.EMA_9)
-        bearish = (df.MACD < df.MACD_signal) & (df.RSI < 50) & (df.EMA_5 < df.EMA_9)
-        df["Signal"] = np.select([bullish, bearish], ["BUY", "SELL"], default="HOLD")
-        last = df.iloc[-1]
-        pip = float(pip_size)
-        entry = float(last.close)
-        tp_distance = float(tp_pips) * pip
-        report = (
-            f"## {symbol} — AF Global Python Terminal\\n"
-            f"**Latest close:** {entry:.8f}  \\n"
-            f"**Signal:** {last.Signal}  \\n"
-            f"**MACD:** {last.MACD:.6f} | **MACD signal:** {last.MACD_signal:.6f}  \\n"
-            f"**RSI({rsi_period}):** {last.RSI:.2f} | **CCI({cci_period}):** {last.CCI:.2f}  \\n"
-            f"**Stochastic:** %K {last.Stoch_K:.2f} / %D {last.Stoch_D:.2f}  \\n"
-            f"**Configured lot:** {float(lot):g}; lot exponent: {float(lot_exp):g}; "
-            f"configured TP: {float(tp_pips):g} pips\\n\\n"
-            f"**Illustrative TP levels from latest close:** BUY {entry+tp_distance:.8f} / "
-            f"SELL {entry-tp_distance:.8f}. These are not validated order levels.\\n\\n"
-            f"Rows analyzed: {len(df)}. {DISCLAIMER}"
-        )
-        cols = [c for c in ["time","open","high","low","close","MACD","MACD_signal","RSI","CCI","Stoch_K","Stoch_D","Signal"] if c in df]
-        return report, df[cols].tail(100)
-    except Exception as e:
-        return f"Could not process file: {e}", None
-
-with gr.Blocks(title=TITLE, theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# AF Global Expert — Python Terminal")
-    gr.Markdown(DISCLAIMER)
-    with gr.Row():
-        file = gr.File(label="OHLC CSV", file_types=[".csv"])
-        symbol = gr.Textbox(label="Symbol", value="EURUSD")
-    with gr.Accordion("Strategy / risk parameters", open=True):
-        with gr.Row():
-            lot = gr.Number(label="Lots", value=0.01)
-            lot_exp = gr.Number(label="Lot exponent", value=1.44)
-            tp = gr.Number(label="Take profit (pips)", value=50)
-            pip = gr.Number(label="Pip size", value=0.0001)
-        with gr.Row():
-            mf = gr.Number(label="MACD fast", value=8, precision=0)
-            ms = gr.Number(label="MACD slow", value=17, precision=0)
-            msi = gr.Number(label="MACD signal", value=9, precision=0)
-            rp = gr.Number(label="RSI period", value=9, precision=0)
-        with gr.Row():
-            cp = gr.Number(label="CCI period", value=13, precision=0)
-            sk = gr.Number(label="Stochastic K", value=5, precision=0)
-            sd = gr.Number(label="Stochastic D", value=3, precision=0)
-    run = gr.Button("Analyze candles", variant="primary")
-    output = gr.Markdown()
-    table = gr.Dataframe(label="Latest candles and indicators", interactive=False)
-    run.click(analyze, inputs=[file,symbol,lot,lot_exp,tp,pip,mf,ms,msi,rp,cp,sk,sd],
-              outputs=[output,table])
-
-demo.launch()