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app.py

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+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()

requirements.txt

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+gradio>=4.44
+pandas>=2.0
+numpy>=1.24