Add files via upload

analyze_results.py

@@ -0,0 +1,11 @@
+import pandas as pd
+
+# 1) Load the CSV
+df = pd.read_csv("optimization_results.csv")
+
+# 2) Sort by total_pnl descending
+top = df.sort_values("total_pnl", ascending=False).head(10)
+
+# 3) Print to console
+print("Top 10 parameter sets by Total PnL:\n", top.to_string(index=False))
+

app.py

@@ -0,0 +1,220 @@
+import os
+import requests
+import random
+import streamlit as st
+import numpy as np
+import pandas as pd
+from dateutil import parser
+from oandapyV20 import API
+from oandapyV20.endpoints.pricing import PricingInfo
+from cip import theoretical_forward, deviation_bps
+from streamlit_autorefresh import st_autorefresh
+
+# Auto-refresh every 5 seconds
+st_autorefresh(interval=5_000, key="refresh")
+
+# Page configuration
+st.set_page_config(page_title="FX Arbitrage Dashboard", layout="wide")
+
+# --- Credentials via Streamlit secrets & Endpoints ---
+# Create a file at ~/.streamlit/secrets.toml (or ./fx_arbitrage/.streamlit/secrets.toml) with:
+#
+# [oanda]
+# token      = "<YOUR_OANDA_TOKEN>"
+# account_id = "<YOUR_OANDA_ACCOUNT_ID>"
+#
+# [slack]
+# webhook = "<YOUR_SLACK_WEBHOOK_URL>"
+#
+# Streamlit will auto-load this file into st.secrets
+OANDA_TOKEN      = st.secrets["oanda"]["token"]
+OANDA_ACCOUNT_ID = st.secrets["oanda"]["account_id"]
+SLACK_WEBHOOK    = st.secrets.get("slack", {}).get("webhook", "")
+PRACTICE_SWAP_API = "https://api-fxpractice.oanda.com"
+
+# Initialize OANDA client for spot data
+client = API(access_token=OANDA_TOKEN, environment="practice")
+
+# --- Sidebar controls ---
+st.sidebar.header("Settings")
+override_provider = st.sidebar.selectbox(
+    "Forward-Rate Provider", ["Manual", "Swap-Points"]
+)
+manual_bps = st.sidebar.slider(
+    "Manual forward offset (bps)", -10.0, 10.0, 0.0, step=0.1
+)
+threshold_bps = st.sidebar.number_input(
+    "Deviation threshold (bps)", 0.5, 10.0, 1.0, step=0.5
+)
+stop_loss_bps = st.sidebar.number_input(
+    "Stop-loss threshold (bps)", 0.0, 20.0, 2.0, step=0.5
+)
+spread_bps = st.sidebar.number_input(
+    "Spread cost per trade (bps)", 0.0, 5.0, 0.1, step=0.1
+)
+tenor_days = st.sidebar.number_input(
+    "Tenor days", 1, 90, 30
+)
+pairs = st.sidebar.multiselect(
+    "Currency pairs", ["EUR_USD", "GBP_USD", "USD_JPY"], default=["EUR_USD", "GBP_USD", "USD_JPY"]
+)
+
+# Initialize histories
+if 'history' not in st.session_state:
+    st.session_state.history = {pair: [] for pair in pairs}
+if 'fwd_history' not in st.session_state:
+    st.session_state.fwd_history = {pair: [] for pair in pairs}
+
+# --- Utility functions ---
+def fetch_spot(pair):
+    pricing = client.request(
+        PricingInfo(accountID=OANDA_ACCOUNT_ID, params={"instruments": pair})
+    )
+    bid = float(pricing["prices"][0]["bids"][0]["price"])
+    ask = float(pricing["prices"][0]["asks"][0]["price"])
+    return (bid + ask) / 2
+
+
+def fetch_swap_point(pair):
+    """Fetch the daily swap-point for the given tenor."""
+    url = f"{PRACTICE_SWAP_API}/v3/accounts/{OANDA_ACCOUNT_ID}/instruments/{pair}/swap_rates"
+    headers = {"Authorization": f"Bearer {OANDA_TOKEN}"}
+    resp = requests.get(url, headers=headers)
+    if resp.status_code != 200:
+        return 0.0
+    for r in resp.json().get("swapRates", []):
+        if r.get('tenor').endswith('D') and int(r.get('tenor')[:-1]) == tenor_days:
+            lr = float(r.get("longRate", 0))
+            sr = float(r.get("shortRate", 0))
+            return lr - sr
+    return 0.0
+
+
+def simulate_pnl(spot0, obs_fwd, days, sims=500):
+    pnls = []
+    for _ in range(sims):
+        path = spot0
+        daily = []
+        for _ in range(days):
+            shock = random.uniform(-0.005, 0.005)
+            path *= (1 + shock)
+            daily.append(1e6 * (path - obs_fwd))
+        pnls.append(daily)
+    return np.array(pnls)
+
+# --- Data collection & metrics ---
+data_rows = []
+for pair in pairs:
+    spot_mid = fetch_spot(pair)
+
+    # Determine observed forward
+    if override_provider == "Manual":
+        theo_fwd = theoretical_forward(spot_mid, 0.025, 0.005, tenor_days)
+        obs_fwd = theo_fwd * (1 + manual_bps / 10_000)
+    else:
+        swap_pts = fetch_swap_point(pair)
+        obs_fwd = spot_mid + swap_pts * tenor_days / 360
+        theo_fwd = theoretical_forward(spot_mid, 0.025, 0.005, tenor_days)
+
+    dev_bps = deviation_bps(obs_fwd, theo_fwd)
+
+    # update histories
+    hist = st.session_state.history[pair]
+    hist.append(dev_bps)
+    if len(hist) > 50:
+        hist.pop(0)
+    st.session_state.history[pair] = hist
+
+    fh = st.session_state.fwd_history[pair]
+    fh.append(obs_fwd)
+    if len(fh) > 50:
+        fh.pop(0)
+    st.session_state.fwd_history[pair] = fh
+
+    # signal
+    if dev_bps > threshold_bps:
+        sig = "Rich → Sell forward"
+    elif dev_bps < -threshold_bps:
+        sig = "Cheap → Buy forward"
+    else:
+        sig = "No arbitrage"
+
+    # PnL calculation
+    cost = spread_bps / 10_000 * 1_000_000
+    raw_pnl = (obs_fwd - theo_fwd) * 1_000_000
+    pnl = raw_pnl - cost
+    stop_amt = stop_loss_bps / 10_000 * 1_000_000
+    if pnl < -stop_amt:
+        pnl = -stop_amt
+
+    data_rows.append({
+        "Pair":             pair,
+        "Spot Mid":         f"{spot_mid:.6f}",
+        "Observed Forward": f"{obs_fwd:.6f}",
+        "Theoretical Fwd":  f"{theo_fwd:.6f}",
+        "Deviation (bps)":  f"{dev_bps:+.2f}",
+        "Signal":           sig,
+        "PnL ($)":          f"{pnl:,.0f}"
+    })
+
+    if SLACK_WEBHOOK and sig != "No arbitrage":
+        requests.post(SLACK_WEBHOOK, json={"text": f"Arb alert: {pair} {dev_bps:+.2f}bps → {sig}"})
+
+# --- Summary Metrics ---
+col1, col2, col3, col4 = st.columns(4)
+# parse PnL values from data_rows
+pnls = [float(r["PnL ($)"].replace("$","").replace(",","") ) for r in data_rows]
+total_pnl = sum(pnls)
+win_rate  = np.mean([1 if v>0 else 0 for v in pnls]) * 100
+max_dd     = min(pnls)
+current_dev= data_rows[0]["Deviation (bps)"]
+col1.metric("Total PnL", f"${total_pnl:,.0f}")
+col2.metric("Win Rate", f"{win_rate:.1f}%")
+col3.metric("Max Drawdown", f"${max_dd:,.0f}")
+col4.metric("Current Dev", f"{current_dev} bps")
+
+# --- Display dashboard ---
+st.title("FX Arbitrage Dashboard — Live")
+st.dataframe(pd.DataFrame(data_rows), use_container_width=True)
+st.markdown("**Auto-refreshes every 5s**")
+
+# Deviation & Forward History
+st.subheader("Deviation History (bps)")
+for pair in pairs:
+    st.line_chart(pd.DataFrame({pair: st.session_state.history[pair]}))
+
+st.subheader("Observed Forward History")
+for pair in pairs:
+    st.line_chart(pd.DataFrame({pair: st.session_state.fwd_history[pair]}))
+
+# PnL Distribution
+st.subheader(f"PnL Distribution at Day {tenor_days}")
+for pair in pairs:
+    spot_mid = fetch_spot(pair)
+    if override_provider == "Manual":
+        theo_fwd = theoretical_forward(spot_mid, 0.025, 0.005, tenor_days)
+        obs_fwd = theo_fwd * (1 + manual_bps / 10_000)
+    else:
+        swap_pts = fetch_swap_point(pair)
+        obs_fwd = spot_mid + swap_pts * tenor_days / 360
+    sims = simulate_pnl(spot_mid, obs_fwd, tenor_days, sims=1000)
+    st.write(f"{pair} PnL Histogram")
+    st.bar_chart(pd.Series(sims[:, -1], name=pair))
+
+# --- Equity Curve ---
+st.subheader("Equity Curve (last 50 bars)")
+for pair in pairs:
+    # compute per-bar PnL from history and forward history
+    pnl_series = []
+    for obs, dev in zip(st.session_state.fwd_history[pair], st.session_state.history[pair]):
+        spot_val = fetch_spot(pair)
+        theo_val = theoretical_forward(spot_val, 0.025, 0.005, tenor_days)
+        raw = (obs - theo_val) * 1_000_000
+        cost = spread_bps/10_000 * 1_000_000
+        pnl_val = raw - cost
+        stop_amt = stop_loss_bps/10_000 * 1_000_000
+        pnl_series.append(max(pnl_val, -stop_amt))
+    equity = np.cumsum(pnl_series)
+    st.line_chart(pd.DataFrame({pair: equity}))
+
+

backtest.py

@@ -0,0 +1,144 @@
+import os
+import requests
+import pandas as pd
+import matplotlib.pyplot as plt
+from dateutil import parser
+from oandapyV20 import API
+from oandapyV20.endpoints.instruments import InstrumentsCandles
+from cip import theoretical_forward, deviation_bps
+
+# === Configuration ===
+OANDA_TOKEN      = os.getenv("OANDA_TOKEN")
+OANDA_ACCOUNT_ID = os.getenv("OANDA_ACCOUNT_ID")
+BASE_URL         = "https://api-fxtrade.oanda.com"  # production for swap rates
+
+if not OANDA_TOKEN or not OANDA_ACCOUNT_ID:
+    raise RuntimeError("Please set OANDA_TOKEN and OANDA_ACCOUNT_ID environment variables")
+
+# Initialize OANDA API client (practice for spot data)
+api = API(access_token=OANDA_TOKEN, environment="practice")
+
+# === Data Fetching ===
+
+def fetch_spot_history(pair: str, days: int = 365) -> pd.Series:
+    """
+    Fetch daily historical spot mid-prices for the FX pair.
+    Returns a pandas Series indexed by date.
+    """
+    req = InstrumentsCandles(
+        instrument=pair,
+        params={"granularity": "D", "count": days, "price": "M"}
+    )
+    data = api.request(req)["candles"]
+    records = []
+    for c in data:
+        dt = parser.isoparse(c["time"])  # full timestamp
+        o = float(c["mid"]["o"])
+        c_ = float(c["mid"]["c"])
+        records.append((dt.date(), (o + c_) / 2))
+    series = pd.Series({d: s for d, s in records}).sort_index()
+    return series
+
+
+def fetch_swap_history(pair: str, days: int = 365) -> pd.Series:
+    """
+    Fetch daily historical swap-rates (forward-points) for the FX pair.
+    Returns a pandas Series of daily forward-points (decimal) indexed by date.
+    Falls back to zeros if endpoint unavailable (e.g., practice account).
+    """
+    url = f"{BASE_URL}/v3/accounts/{OANDA_ACCOUNT_ID}/instruments/{pair}/swap_rates"
+    headers = {"Authorization": f"Bearer {OANDA_TOKEN}"}
+    params = {"count": days, "granularity": "D"}
+    try:
+        resp = requests.get(url, headers=headers, params=params)
+        resp.raise_for_status()
+        data = resp.json().get("swapRates", [])
+        records = []
+        for r in data:
+            dt = parser.isoparse(r["time"]).date()
+            long_rate = float(r.get("longRate", 0))
+            short_rate = float(r.get("shortRate", 0))
+            records.append((dt, long_rate - short_rate))
+        series = pd.Series({d: p for d, p in records}).sort_index()
+    except Exception:
+        # Practice environment may not support swap_rates; fallback to zeros
+        print("Warning: swap_rates endpoint unavailable, falling back to zeros.")
+        # Build zero series over requested date range
+        df_spot = fetch_spot_history(pair, days=days)
+        series = pd.Series(0.0, index=df_spot.index)
+    return series
+
+# === Backtest ===
+
+def backtest(
+    pair: str,
+    tenor_days: int = 30,
+    r_dom: float = 0.025,
+    r_for: float = 0.005,
+    notional: float = 1_000_000,
+    spread_bps: float = 0.5,
+    stop_loss_bps: float = 5.0,
+    history_days: int = 365
+) -> None:
+    """
+    Back-test FX CIP arbitrage using real swap-points.
+    """
+    # Fetch data
+    spot = fetch_spot_history(pair, days=history_days)
+    swap_pts = fetch_swap_history(pair, days=history_days)
+
+    # Build DataFrame
+    df = pd.DataFrame({"spot": spot})
+    # theoretical forward
+    df["theo_fwd"] = df["spot"].apply(lambda s: theoretical_forward(s, r_dom, r_for, tenor_days))
+    # observed forward = spot + tenor * swap_pts/360
+    df["swap_pts"] = swap_pts.reindex(df.index).fillna(method="ffill")
+    df["obs_fwd"] = df["spot"] + df["swap_pts"] * tenor_days / 360
+
+    # deviation and signal
+    df["dev_bps"] = (df["obs_fwd"] - df["theo_fwd"]) / df["theo_fwd"] * 10_000
+    df["signal"] = 0
+    df.loc[df["dev_bps"] > 0, "signal"] = -1  # sell forward if rich
+    df.loc[df["dev_bps"] < 0, "signal"] = +1  # buy forward if cheap
+
+    # PnL with spread cost & stop-loss
+    cost = spread_bps / 10_000 * notional
+    df["exit_spot"] = df["spot"].shift(-tenor_days)
+    df["raw_pnl"] = df["signal"] * (df["exit_spot"] - df["obs_fwd"]) * notional
+    df["pnl"] = df["raw_pnl"] - df["signal"].abs() * cost
+    stop_amt = stop_loss_bps / 10_000 * notional
+    df.loc[df["pnl"] < -stop_amt, "pnl"] = -stop_amt
+
+    # drop incomplete
+    trades = df.dropna(subset=["pnl"])
+
+    # metrics
+    total_pnl    = trades["pnl"].sum()
+    num_trades   = (trades["signal"] != 0).sum()
+    win_rate     = trades["pnl"].gt(0).mean() * 100 if num_trades else 0
+    avg_pnl      = trades["pnl"].mean() if num_trades else 0
+    equity = trades["pnl"].cumsum()
+    max_dd = (equity.cummax() - equity).max() if not equity.empty else 0
+
+    # output
+    print(f"=== Backtest Results for {pair} ({tenor_days}d tenor) ===")
+    print(f"Total PnL            : ${total_pnl:,.0f}")
+    print(f"Number of trades     : {num_trades}")
+    print(f"Win rate             : {win_rate:.1f}%")
+    print(f"Average PnL/trade    : ${avg_pnl:,.0f}")
+    print(f"Max Drawdown         : ${max_dd:,.0f}")
+
+    # plot equity
+    plt.figure(figsize=(10, 4))
+    plt.plot(equity.index, equity.values)
+    plt.title(f"Equity Curve ({pair}, {tenor_days}d)")
+    plt.xlabel("Date")
+    plt.ylabel("Cumulative PnL ($)")
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+# === Main ===
+if __name__ == "__main__":
+    backtest("EUR_USD")
+

cip.py

@@ -0,0 +1,27 @@
+def theoretical_forward(spot: float, r_dom: float, r_for: float, tenor_days: int) -> float:
+    """
+    Calculate the theoretical forward rate:
+      F = S * (1 + r_dom * (tenor_days/360)) / (1 + r_for * (tenor_days/360))
+    """
+    return spot * (1 + r_dom * tenor_days / 360) / (1 + r_for * tenor_days / 360)
+
+def deviation_bps(obs_fwd: float, theo_fwd: float) -> float:
+    """
+    Compute the deviation between observed and theoretical forward,
+    expressed in basis points.
+    """
+    return (obs_fwd - theo_fwd) / theo_fwd * 10_000
+
+if __name__ == "__main__":
+    # Example inputs (replace these with your real data)
+    spot_rate      = 1.16910            # from PricingInfo closeout or mid
+    observed_fwd   = 1.16930            # placeholder forward outright
+    r_domestic     = 0.025              # e.g., 2.5% annual domestic interest
+    r_foreign      = 0.005              # e.g., 0.5% annual foreign interest
+    tenor_in_days  = 30                 # for 1M tenor, approx 30 days
+
+    theo = theoretical_forward(spot_rate, r_domestic, r_foreign, tenor_in_days)
+    dev  = deviation_bps(observed_fwd, theo)
+
+    print(f"Theoretical 1M Forward: {theo:.6f}")
+    print(f"Deviation: {dev:.2f} bps")

eur_usd_pricing.json

@@ -0,0 +1,74 @@
+{
+  "time": "2025-07-22T10:40:15.470477032Z",
+  "prices": [
+    {
+      "type": "PRICE",
+      "time": "2025-07-22T10:40:10.297022351Z",
+      "bids": [
+        {
+          "price": "1.17025",
+          "liquidity": 500000
+        },
+        {
+          "price": "1.17024",
+          "liquidity": 500000
+        },
+        {
+          "price": "1.17023",
+          "liquidity": 2000000
+        },
+        {
+          "price": "1.17022",
+          "liquidity": 2000000
+        },
+        {
+          "price": "1.17021",
+          "liquidity": 5000000
+        },
+        {
+          "price": "1.17019",
+          "liquidity": 10000000
+        },
+        {
+          "price": "1.17016",
+          "liquidity": 10000000
+        }
+      ],
+      "asks": [
+        {
+          "price": "1.17032",
+          "liquidity": 500000
+        },
+        {
+          "price": "1.17034",
+          "liquidity": 2500000
+        },
+        {
+          "price": "1.17035",
+          "liquidity": 2000000
+        },
+        {
+          "price": "1.17036",
+          "liquidity": 5000000
+        },
+        {
+          "price": "1.17039",
+          "liquidity": 10000000
+        },
+        {
+          "price": "1.17042",
+          "liquidity": 10000000
+        }
+      ],
+      "closeoutBid": "1.17016",
+      "closeoutAsk": "1.17042",
+      "status": "tradeable",
+      "tradeable": true,
+      "quoteHomeConversionFactors": {
+        "positiveUnits": "0.74144374",
+        "negativeUnits": "0.74155370"
+      },
+      "instrument": "EUR_USD"
+    }
+  ]
+}

list_accounts.py

@@ -0,0 +1,17 @@
+import os, json
+from oandapyV20 import API
+from oandapyV20.endpoints.accounts import AccountList
+
+# 1. Read your practice token from env
+token = os.getenv("OANDA_TOKEN")
+
+# 2. Initialize the client in practice mode
+client = API(access_token=token, environment="practice")
+
+# 3. Create and send the AccountList request
+req = AccountList()
+resp = client.request(req)
+
+# 4. Pretty-print the JSON so you can see your account IDs
+print(json.dumps(resp, indent=2))
+  

oanda_test.py

@@ -0,0 +1,92 @@
+import warnings
+from urllib3.exceptions import NotOpenSSLWarning
+
+# Silence the LibreSSL/OpenSSL warning
+warnings.filterwarnings("ignore", category=NotOpenSSLWarning)
+
+import os
+import json
+import requests
+from oandapyV20 import API
+from oandapyV20.endpoints.pricing import PricingInfo
+from cip import theoretical_forward, deviation_bps
+
+# 1. Read credentials from environment variables
+#    Ensure OANDA_TOKEN and OANDA_ACCOUNT_ID are exported in the same shell
+token = os.getenv("OANDA_TOKEN")
+account_id = os.getenv("OANDA_ACCOUNT_ID")
+
+# Debug: verify credentials are loaded (remove after confirming)
+print("DEBUG: token →", token)
+print("DEBUG: account_id →", account_id)
+
+# 2. Initialize OANDA client (practice environment)
+client = API(access_token=token, environment="practice")
+
+# 3. Fetch spot pricing for EUR/USD
+pricing_req = PricingInfo(accountID=account_id, params={"instruments": "EUR_USD"})
+pricing_resp = client.request(pricing_req)
+print("\nSPOT PRICING:")
+print(json.dumps(pricing_resp, indent=2))
+
+# 4. Compute spot mid price
+bid = float(pricing_resp["prices"][0]["bids"][0]["price"])
+ask = float(pricing_resp["prices"][0]["asks"][0]["price"])
+spot_mid = (bid + ask) / 2
+print(f"Spot mid: {spot_mid:.6f}")
+
+# 5. Fetch all swap rates for EUR/USD via correct endpoint
+swap_url = "https://api-fxpractice.oanda.com/v3/instruments/EUR_USD/swap_rates"
+headers = {
+    "Authorization": f"Bearer {token}",
+    "Content-Type": "application/json"
+}
+swap_resp = requests.get(swap_url, headers=headers)
+swap_data = swap_resp.json()
+print("\nSWAP RATES RESPONSE:")
+print(json.dumps(swap_data, indent=2))
+
+# 6. Extract 1M swap-rate if available
+days = 30  # tenor in days for 1M
+swap_rates = swap_data.get("swapRates", [])
+if swap_rates:
+    rate_1m = next((r for r in swap_rates if r.get("tenor") == "1M"), None)
+    if rate_1m:
+        print("\nObserved market 1M swap-rate object:")
+        print(json.dumps(rate_1m, indent=2))
+        # Compute observed forward outright: spot_mid + swap points
+        fwd_pts = (rate_1m["longRate"] - rate_1m["shortRate"]) * days / 360
+        obs_fwd = spot_mid + fwd_pts
+        print(f"Observed 1M forward (spot + swap points): {obs_fwd:.6f}")
+    else:
+        print("\n⚠️ 1M tenor not found in swapRates; falling back to theoretical CIP")
+        # placeholder interest rates
+        r_domestic = 0.025  # e.g., USD OIS
+        r_foreign = 0.005   # e.g., EUR OIS
+        obs_fwd = theoretical_forward(spot_mid, r_domestic, r_foreign, days)
+        print(f"Fallback observed forward: {obs_fwd:.6f}")
+else:
+    print("\n⚠️ No swapRates data; using theoretical CIP as observed forward")
+    # placeholder interest rates
+    r_domestic = 0.025
+    r_foreign = 0.005
+    obs_fwd = theoretical_forward(spot_mid, r_domestic, r_foreign, days)
+    print(f"Fallback observed forward: {obs_fwd:.6f}")
+
+# 7. Compute theoretical forward and deviation
+# placeholder interest rates (update with live data when available)
+r_domestic = 0.025
+r_foreign = 0.005
+theo_fwd = theoretical_forward(spot_mid, r_domestic, r_foreign, days)
+dev_bps = deviation_bps(obs_fwd, theo_fwd)
+print(f"\nTheoretical 1M Forward: {theo_fwd:.6f}")
+print(f"Deviation         : {dev_bps:.2f} bps")
+
+# 8. Flag arbitrage signal if deviation exceeds threshold
+threshold = 2.0  # bps
+if abs(dev_bps) > threshold:
+    direction = "Sell forward / Buy spot" if dev_bps > 0 else "Buy forward / Sell spot"
+    print(f"⚠️ Arbitrage signal: {dev_bps:.2f} bps → {direction}")
+else:
+    print("✅ No actionable arbitrage (deviation within threshold).")
+

optimization_results.csv

@@ -0,0 +1,226 @@
+threshold_bps,offset_bps,stop_loss_bps,spread_bps,total_pnl,num_trades,win_rate,avg_pnl,max_drawdown
+0.5,1.0,2.0,0.1,3035914.489215958,335,44.47761194029851,9062.431311092412,14800.0
+0.5,1.0,2.0,0.5,3029914.489215958,335,44.47761194029851,9044.520863331218,14800.0
+0.5,1.0,2.0,1.0,3022414.489215958,335,44.47761194029851,9022.132803629725,14800.0
+0.5,1.0,5.0,0.1,2980414.489215958,335,44.47761194029851,8896.759669301367,37000.0
+0.5,1.0,5.0,0.5,2974414.489215958,335,44.47761194029851,8878.849221540173,37000.0
+0.5,1.0,5.0,1.0,2966914.489215958,335,44.47761194029851,8856.46116183868,37000.0
+0.5,1.0,10.0,0.1,2888282.456922122,335,44.47761194029851,8621.738677379468,75079.10061016213
+0.5,1.0,10.0,0.5,2882242.456922122,335,44.47761194029851,8603.7088266332,75399.10061016213
+0.5,1.0,10.0,1.0,2874692.456922122,335,44.47761194029851,8581.171513200363,75799.10061016213
+0.5,2.0,2.0,0.1,3052218.607753026,335,44.776119402985074,9111.100321650823,14800.0
+0.5,2.0,2.0,0.5,3046218.607753026,335,44.47761194029851,9093.189873889629,14800.0
+0.5,2.0,2.0,1.0,3038718.607753026,335,44.47761194029851,9070.801814188137,14800.0
+0.5,2.0,5.0,0.1,2996718.607753026,335,44.776119402985074,8945.428679859779,37000.0
+0.5,2.0,5.0,0.5,2990718.607753026,335,44.47761194029851,8927.518232098584,37000.0
+0.5,2.0,5.0,1.0,2983218.607753026,335,44.47761194029851,8905.130172397092,37000.0
+0.5,2.0,10.0,0.1,2904773.3181278696,335,44.776119402985074,8670.96512873991,74165.65436484851
+0.5,2.0,10.0,0.5,2898693.3181278696,335,44.47761194029851,8652.815875008566,74485.65436484851
+0.5,2.0,10.0,1.0,2891110.4573677694,335,44.47761194029851,8630.180469754536,74885.65436484851
+0.5,3.0,2.0,0.1,3068522.72629009,335,44.776119402985074,9159.769332209224,14800.0
+0.5,3.0,2.0,0.5,3062522.72629009,335,44.776119402985074,9141.85888444803,14800.0
+0.5,3.0,2.0,1.0,3055022.72629009,335,44.776119402985074,9119.470824746537,14800.0
+0.5,3.0,5.0,0.1,3013118.457813414,335,44.776119402985074,8994.383456159445,37000.0
+0.5,3.0,5.0,0.5,3007078.457813414,335,44.776119402985074,8976.353605413176,37000.0
+0.5,3.0,5.0,1.0,2999528.457813414,335,44.776119402985074,8953.81629198034,37000.0
+0.5,3.0,10.0,0.1,2921299.887645775,335,44.776119402985074,8720.29817207694,74000.0
+0.5,3.0,10.0,0.5,2915219.887645775,335,44.776119402985074,8702.148918345598,74000.0
+0.5,3.0,10.0,1.0,2907619.887645775,335,44.776119402985074,8679.462351181417,74000.0
+0.5,4.0,2.0,0.1,3084826.8448271556,335,44.776119402985074,9208.43834276763,14800.0
+0.5,4.0,2.0,0.5,3078826.8448271556,335,44.776119402985074,9190.527895006435,14800.0
+0.5,4.0,2.0,1.0,3071326.8448271556,335,44.776119402985074,9168.139835304943,14800.0
+0.5,4.0,5.0,0.1,3029536.458259059,335,44.776119402985074,9043.392412713609,37000.0
+0.5,4.0,5.0,0.5,3023496.458259059,335,44.776119402985074,9025.36256196734,37000.0
+0.5,4.0,5.0,1.0,3015946.458259059,335,44.776119402985074,9002.825248534506,37000.0
+0.5,4.0,10.0,0.1,2937917.0644606417,335,44.776119402985074,8769.90168495714,74000.0
+0.5,4.0,10.0,0.5,2931797.0644606417,335,44.776119402985074,8751.633028240722,74000.0
+0.5,4.0,10.0,1.0,2924147.0644606417,335,44.776119402985074,8728.797207345198,74000.0
+0.5,5.0,2.0,0.1,3101154.4587047044,335,44.776119402985074,9257.177488670759,14800.0
+0.5,5.0,2.0,0.5,3095130.963364221,335,44.776119402985074,9239.196905564839,14800.0
+0.5,5.0,2.0,1.0,3087630.963364221,335,44.776119402985074,9216.808845863347,14800.0
+0.5,5.0,5.0,0.1,3045954.4587047044,335,44.776119402985074,9092.401369267774,37000.0
+0.5,5.0,5.0,0.5,3039914.4587047044,335,44.776119402985074,9074.371518521506,37000.0
+0.5,5.0,5.0,1.0,3032364.4587047044,335,44.776119402985074,9051.83420508867,37000.0
+0.5,5.0,10.0,0.1,2954551.9901967905,335,44.776119402985074,8819.558179691912,74000.0
+0.5,5.0,10.0,0.5,2948431.9901967905,335,44.776119402985074,8801.289522975494,74000.0
+0.5,5.0,10.0,1.0,2940781.9901967905,335,44.776119402985074,8778.453702079973,74000.0
+1.0,1.0,2.0,0.1,1233700.1759173332,160,19.1044776119403,3682.687092290547,7200.0
+1.0,1.0,2.0,0.5,1231100.1759173332,160,19.1044776119403,3674.925898260696,7200.0
+1.0,1.0,2.0,1.0,1227850.1759173332,160,19.1044776119403,3665.224405723383,7200.0
+1.0,1.0,5.0,0.1,1205200.1759173332,160,19.1044776119403,3597.612465424875,18000.0
+1.0,1.0,5.0,0.5,1202600.1759173332,160,19.1044776119403,3589.8512713950245,18000.0
+1.0,1.0,5.0,1.0,1199350.1759173332,160,19.1044776119403,3580.149778857711,18000.0
+1.0,1.0,10.0,0.1,1157700.1759173332,160,19.1044776119403,3455.821420648756,42775.720814244356
+1.0,1.0,10.0,0.5,1155100.1759173332,160,19.1044776119403,3448.0602266189053,42895.720814244356
+1.0,1.0,10.0,1.0,1151850.1759173332,160,19.1044776119403,3438.3587340815916,43045.720814244356
+1.0,2.0,2.0,0.1,3052218.607753026,335,44.776119402985074,9111.100321650823,14800.0
+1.0,2.0,2.0,0.5,3046218.607753026,335,44.47761194029851,9093.189873889629,14800.0
+1.0,2.0,2.0,1.0,3038718.607753026,335,44.47761194029851,9070.801814188137,14800.0
+1.0,2.0,5.0,0.1,2996718.607753026,335,44.776119402985074,8945.428679859779,37000.0
+1.0,2.0,5.0,0.5,2990718.607753026,335,44.47761194029851,8927.518232098584,37000.0
+1.0,2.0,5.0,1.0,2983218.607753026,335,44.47761194029851,8905.130172397092,37000.0
+1.0,2.0,10.0,0.1,2904773.3181278696,335,44.776119402985074,8670.96512873991,74165.65436484851
+1.0,2.0,10.0,0.5,2898693.3181278696,335,44.47761194029851,8652.815875008566,74485.65436484851
+1.0,2.0,10.0,1.0,2891110.4573677694,335,44.47761194029851,8630.180469754536,74885.65436484851
+1.0,3.0,2.0,0.1,3068522.72629009,335,44.776119402985074,9159.769332209224,14800.0
+1.0,3.0,2.0,0.5,3062522.72629009,335,44.776119402985074,9141.85888444803,14800.0
+1.0,3.0,2.0,1.0,3055022.72629009,335,44.776119402985074,9119.470824746537,14800.0
+1.0,3.0,5.0,0.1,3013118.457813414,335,44.776119402985074,8994.383456159445,37000.0
+1.0,3.0,5.0,0.5,3007078.457813414,335,44.776119402985074,8976.353605413176,37000.0
+1.0,3.0,5.0,1.0,2999528.457813414,335,44.776119402985074,8953.81629198034,37000.0
+1.0,3.0,10.0,0.1,2921299.887645775,335,44.776119402985074,8720.29817207694,74000.0
+1.0,3.0,10.0,0.5,2915219.887645775,335,44.776119402985074,8702.148918345598,74000.0
+1.0,3.0,10.0,1.0,2907619.887645775,335,44.776119402985074,8679.462351181417,74000.0
+1.0,4.0,2.0,0.1,3084826.8448271556,335,44.776119402985074,9208.43834276763,14800.0
+1.0,4.0,2.0,0.5,3078826.8448271556,335,44.776119402985074,9190.527895006435,14800.0
+1.0,4.0,2.0,1.0,3071326.8448271556,335,44.776119402985074,9168.139835304943,14800.0
+1.0,4.0,5.0,0.1,3029536.458259059,335,44.776119402985074,9043.392412713609,37000.0
+1.0,4.0,5.0,0.5,3023496.458259059,335,44.776119402985074,9025.36256196734,37000.0
+1.0,4.0,5.0,1.0,3015946.458259059,335,44.776119402985074,9002.825248534506,37000.0
+1.0,4.0,10.0,0.1,2937917.0644606417,335,44.776119402985074,8769.90168495714,74000.0
+1.0,4.0,10.0,0.5,2931797.0644606417,335,44.776119402985074,8751.633028240722,74000.0
+1.0,4.0,10.0,1.0,2924147.0644606417,335,44.776119402985074,8728.797207345198,74000.0
+1.0,5.0,2.0,0.1,3101154.4587047044,335,44.776119402985074,9257.177488670759,14800.0
+1.0,5.0,2.0,0.5,3095130.963364221,335,44.776119402985074,9239.196905564839,14800.0
+1.0,5.0,2.0,1.0,3087630.963364221,335,44.776119402985074,9216.808845863347,14800.0
+1.0,5.0,5.0,0.1,3045954.4587047044,335,44.776119402985074,9092.401369267774,37000.0
+1.0,5.0,5.0,0.5,3039914.4587047044,335,44.776119402985074,9074.371518521506,37000.0
+1.0,5.0,5.0,1.0,3032364.4587047044,335,44.776119402985074,9051.83420508867,37000.0
+1.0,5.0,10.0,0.1,2954551.9901967905,335,44.776119402985074,8819.558179691912,74000.0
+1.0,5.0,10.0,0.5,2948431.9901967905,335,44.776119402985074,8801.289522975494,74000.0
+1.0,5.0,10.0,1.0,2940781.9901967905,335,44.776119402985074,8778.453702079973,74000.0
+2.0,1.0,2.0,0.1,0.0,0,0.0,0.0,0.0
+2.0,1.0,2.0,0.5,0.0,0,0.0,0.0,0.0
+2.0,1.0,2.0,1.0,0.0,0,0.0,0.0,0.0
+2.0,1.0,5.0,0.1,0.0,0,0.0,0.0,0.0
+2.0,1.0,5.0,0.5,0.0,0,0.0,0.0,0.0
+2.0,1.0,5.0,1.0,0.0,0,0.0,0.0,0.0
+2.0,1.0,10.0,0.1,0.0,0,0.0,0.0,0.0
+2.0,1.0,10.0,0.5,0.0,0,0.0,0.0,0.0
+2.0,1.0,10.0,1.0,0.0,0,0.0,0.0,0.0
+2.0,2.0,2.0,0.1,1234607.5833340343,130,18.507462686567163,3685.395771146371,5800.0
+2.0,2.0,2.0,0.5,1232127.5833340343,130,18.507462686567163,3677.9927860717444,5800.0
+2.0,2.0,2.0,1.0,1229027.5833340343,130,18.507462686567163,3668.739054728461,5800.0
+2.0,2.0,5.0,0.1,1214207.5833340343,130,18.507462686567163,3624.5002487583115,14500.0
+2.0,2.0,5.0,0.5,1211727.5833340343,130,18.507462686567163,3617.0972636836846,14500.0
+2.0,2.0,5.0,1.0,1208627.5833340343,130,18.507462686567163,3607.843532340401,14500.0
+2.0,2.0,10.0,0.1,1180207.5833340343,130,18.507462686567163,3523.0077114448786,29000.0
+2.0,2.0,10.0,0.5,1177727.5833340343,130,18.507462686567163,3515.6047263702517,29000.0
+2.0,2.0,10.0,1.0,1174627.5833340343,130,18.507462686567163,3506.350995026968,29000.0
+2.0,3.0,2.0,0.1,3068522.72629009,335,44.776119402985074,9159.769332209224,14800.0
+2.0,3.0,2.0,0.5,3062522.72629009,335,44.776119402985074,9141.85888444803,14800.0
+2.0,3.0,2.0,1.0,3055022.72629009,335,44.776119402985074,9119.470824746537,14800.0
+2.0,3.0,5.0,0.1,3013118.457813414,335,44.776119402985074,8994.383456159445,37000.0
+2.0,3.0,5.0,0.5,3007078.457813414,335,44.776119402985074,8976.353605413176,37000.0
+2.0,3.0,5.0,1.0,2999528.457813414,335,44.776119402985074,8953.81629198034,37000.0
+2.0,3.0,10.0,0.1,2921299.887645775,335,44.776119402985074,8720.29817207694,74000.0
+2.0,3.0,10.0,0.5,2915219.887645775,335,44.776119402985074,8702.148918345598,74000.0
+2.0,3.0,10.0,1.0,2907619.887645775,335,44.776119402985074,8679.462351181417,74000.0
+2.0,4.0,2.0,0.1,3084826.8448271556,335,44.776119402985074,9208.43834276763,14800.0
+2.0,4.0,2.0,0.5,3078826.8448271556,335,44.776119402985074,9190.527895006435,14800.0
+2.0,4.0,2.0,1.0,3071326.8448271556,335,44.776119402985074,9168.139835304943,14800.0
+2.0,4.0,5.0,0.1,3029536.458259059,335,44.776119402985074,9043.392412713609,37000.0
+2.0,4.0,5.0,0.5,3023496.458259059,335,44.776119402985074,9025.36256196734,37000.0
+2.0,4.0,5.0,1.0,3015946.458259059,335,44.776119402985074,9002.825248534506,37000.0
+2.0,4.0,10.0,0.1,2937917.0644606417,335,44.776119402985074,8769.90168495714,74000.0
+2.0,4.0,10.0,0.5,2931797.0644606417,335,44.776119402985074,8751.633028240722,74000.0
+2.0,4.0,10.0,1.0,2924147.0644606417,335,44.776119402985074,8728.797207345198,74000.0
+2.0,5.0,2.0,0.1,3101154.4587047044,335,44.776119402985074,9257.177488670759,14800.0
+2.0,5.0,2.0,0.5,3095130.963364221,335,44.776119402985074,9239.196905564839,14800.0
+2.0,5.0,2.0,1.0,3087630.963364221,335,44.776119402985074,9216.808845863347,14800.0
+2.0,5.0,5.0,0.1,3045954.4587047044,335,44.776119402985074,9092.401369267774,37000.0
+2.0,5.0,5.0,0.5,3039914.4587047044,335,44.776119402985074,9074.371518521506,37000.0
+2.0,5.0,5.0,1.0,3032364.4587047044,335,44.776119402985074,9051.83420508867,37000.0
+2.0,5.0,10.0,0.1,2954551.9901967905,335,44.776119402985074,8819.558179691912,74000.0
+2.0,5.0,10.0,0.5,2948431.9901967905,335,44.776119402985074,8801.289522975494,74000.0
+2.0,5.0,10.0,1.0,2940781.9901967905,335,44.776119402985074,8778.453702079973,74000.0
+3.0,1.0,2.0,0.1,0.0,0,0.0,0.0,0.0
+3.0,1.0,2.0,0.5,0.0,0,0.0,0.0,0.0
+3.0,1.0,2.0,1.0,0.0,0,0.0,0.0,0.0
+3.0,1.0,5.0,0.1,0.0,0,0.0,0.0,0.0
+3.0,1.0,5.0,0.5,0.0,0,0.0,0.0,0.0
+3.0,1.0,5.0,1.0,0.0,0,0.0,0.0,0.0
+3.0,1.0,10.0,0.1,0.0,0,0.0,0.0,0.0
+3.0,1.0,10.0,0.5,0.0,0,0.0,0.0,0.0
+3.0,1.0,10.0,1.0,0.0,0,0.0,0.0,0.0
+3.0,2.0,2.0,0.1,0.0,0,0.0,0.0,0.0
+3.0,2.0,2.0,0.5,0.0,0,0.0,0.0,0.0
+3.0,2.0,2.0,1.0,0.0,0,0.0,0.0,0.0
+3.0,2.0,5.0,0.1,0.0,0,0.0,0.0,0.0
+3.0,2.0,5.0,0.5,0.0,0,0.0,0.0,0.0
+3.0,2.0,5.0,1.0,0.0,0,0.0,0.0,0.0
+3.0,2.0,10.0,0.1,0.0,0,0.0,0.0,0.0
+3.0,2.0,10.0,0.5,0.0,0,0.0,0.0,0.0
+3.0,2.0,10.0,1.0,0.0,0,0.0,0.0,0.0
+3.0,3.0,2.0,0.1,1136323.1076457775,116,16.119402985074625,3392.0092765545596,5000.0
+3.0,3.0,2.0,0.5,1134163.1076457775,116,16.119402985074625,3385.56151536053,5000.0
+3.0,3.0,2.0,1.0,1131463.1076457775,116,16.119402985074625,3377.5018138679925,5000.0
+3.0,3.0,5.0,0.1,1117723.1076457775,116,16.119402985074625,3336.4868884948582,12500.0
+3.0,3.0,5.0,0.5,1115563.1076457775,116,16.119402985074625,3330.0391273008286,12500.0
+3.0,3.0,5.0,1.0,1112863.1076457775,116,16.119402985074625,3321.979425808291,12500.0
+3.0,3.0,10.0,0.1,1086723.1076457775,116,16.119402985074625,3243.9495750620226,25000.0
+3.0,3.0,10.0,0.5,1084563.1076457775,116,16.119402985074625,3237.5018138679925,25000.0
+3.0,3.0,10.0,1.0,1081863.1076457775,116,16.119402985074625,3229.4421123754555,25000.0
+3.0,4.0,2.0,0.1,3084826.8448271556,335,44.776119402985074,9208.43834276763,14800.0
+3.0,4.0,2.0,0.5,3078826.8448271556,335,44.776119402985074,9190.527895006435,14800.0
+3.0,4.0,2.0,1.0,3071326.8448271556,335,44.776119402985074,9168.139835304943,14800.0
+3.0,4.0,5.0,0.1,3029536.458259059,335,44.776119402985074,9043.392412713609,37000.0
+3.0,4.0,5.0,0.5,3023496.458259059,335,44.776119402985074,9025.36256196734,37000.0
+3.0,4.0,5.0,1.0,3015946.458259059,335,44.776119402985074,9002.825248534506,37000.0
+3.0,4.0,10.0,0.1,2937917.0644606417,335,44.776119402985074,8769.90168495714,74000.0
+3.0,4.0,10.0,0.5,2931797.0644606417,335,44.776119402985074,8751.633028240722,74000.0
+3.0,4.0,10.0,1.0,2924147.0644606417,335,44.776119402985074,8728.797207345198,74000.0
+3.0,5.0,2.0,0.1,3101154.4587047044,335,44.776119402985074,9257.177488670759,14800.0
+3.0,5.0,2.0,0.5,3095130.963364221,335,44.776119402985074,9239.196905564839,14800.0
+3.0,5.0,2.0,1.0,3087630.963364221,335,44.776119402985074,9216.808845863347,14800.0
+3.0,5.0,5.0,0.1,3045954.4587047044,335,44.776119402985074,9092.401369267774,37000.0
+3.0,5.0,5.0,0.5,3039914.4587047044,335,44.776119402985074,9074.371518521506,37000.0
+3.0,5.0,5.0,1.0,3032364.4587047044,335,44.776119402985074,9051.83420508867,37000.0
+3.0,5.0,10.0,0.1,2954551.9901967905,335,44.776119402985074,8819.558179691912,74000.0
+3.0,5.0,10.0,0.5,2948431.9901967905,335,44.776119402985074,8801.289522975494,74000.0
+3.0,5.0,10.0,1.0,2940781.9901967905,335,44.776119402985074,8778.453702079973,74000.0
+4.0,1.0,2.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,1.0,2.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,1.0,2.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,1.0,5.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,1.0,5.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,1.0,5.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,1.0,10.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,1.0,10.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,1.0,10.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,2.0,2.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,2.0,2.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,2.0,2.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,2.0,5.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,2.0,5.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,2.0,5.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,2.0,10.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,2.0,10.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,2.0,10.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,3.0,2.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,3.0,2.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,3.0,2.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,3.0,5.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,3.0,5.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,3.0,5.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,3.0,10.0,0.1,0.0,0,0.0,0.0,0.0
+4.0,3.0,10.0,0.5,0.0,0,0.0,0.0,0.0
+4.0,3.0,10.0,1.0,0.0,0,0.0,0.0,0.0
+4.0,4.0,2.0,0.1,744222.7130903826,90,11.343283582089553,2221.5603375832316,6948.339329446317
+4.0,4.0,2.0,0.5,742702.7130903827,90,11.343283582089553,2217.0230241503964,6988.339329446317
+4.0,4.0,2.0,1.0,740802.7130903827,90,11.343283582089553,2211.3513823593516,7038.339329446317
+4.0,4.0,5.0,0.1,728622.7130903827,90,11.343283582089553,2174.9931734041274,17748.339329446317
+4.0,4.0,5.0,0.5,727102.7130903827,90,11.343283582089553,2170.455859971292,17788.339329446317
+4.0,4.0,5.0,1.0,725202.7130903827,90,11.343283582089553,2164.784218180247,17838.339329446317
+4.0,4.0,10.0,0.1,702713.3203873425,90,11.343283582089553,2097.6517026487836,35748.33932944632
+4.0,4.0,10.0,0.5,701153.3203873425,90,11.343283582089553,2092.994986230873,35788.33932944632
+4.0,4.0,10.0,1.0,699203.3203873425,90,11.343283582089553,2087.174090708485,35838.33932944632
+4.0,5.0,2.0,0.1,3101154.4587047044,335,44.776119402985074,9257.177488670759,14800.0
+4.0,5.0,2.0,0.5,3095130.963364221,335,44.776119402985074,9239.196905564839,14800.0
+4.0,5.0,2.0,1.0,3087630.963364221,335,44.776119402985074,9216.808845863347,14800.0
+4.0,5.0,5.0,0.1,3045954.4587047044,335,44.776119402985074,9092.401369267774,37000.0
+4.0,5.0,5.0,0.5,3039914.4587047044,335,44.776119402985074,9074.371518521506,37000.0
+4.0,5.0,5.0,1.0,3032364.4587047044,335,44.776119402985074,9051.83420508867,37000.0
+4.0,5.0,10.0,0.1,2954551.9901967905,335,44.776119402985074,8819.558179691912,74000.0
+4.0,5.0,10.0,0.5,2948431.9901967905,335,44.776119402985074,8801.289522975494,74000.0
+4.0,5.0,10.0,1.0,2940781.9901967905,335,44.776119402985074,8778.453702079973,74000.0

optimization_results_real.csv

@@ -0,0 +1,37 @@
+threshold_bps,stop_loss_bps,spread_bps,total_pnl,num_trades,win_rate,avg_pnl,max_drawdown
+0.5,2.0,0.1,5153890.000000001,335,57.611940298507456,15384.74626865672,18295.0
+0.5,2.0,0.5,5146160.000000002,335,57.611940298507456,15361.67164179105,18335.0
+0.5,2.0,1.0,5136510.000000002,335,57.611940298507456,15332.865671641797,18385.0
+0.5,5.0,0.1,5111590.000000001,335,57.611940298507456,15258.477611940301,46495.0
+0.5,5.0,0.5,5103830.000000001,335,57.611940298507456,15235.313432835823,46535.0
+0.5,5.0,1.0,5094130.000000001,335,57.611940298507456,15206.358208955227,46585.0
+0.5,10.0,0.1,5042665.0,335,57.611940298507456,15052.731343283582,92680.00000000047
+0.5,10.0,0.5,5034665.0,335,57.611940298507456,15028.850746268658,92840.00000000047
+0.5,10.0,1.0,5024730.000000001,335,57.611940298507456,14999.194029850749,93025.00000000023
+1.0,2.0,0.1,5153890.000000001,335,57.611940298507456,15384.74626865672,18295.0
+1.0,2.0,0.5,5146160.000000002,335,57.611940298507456,15361.67164179105,18335.0
+1.0,2.0,1.0,5136510.000000002,335,57.611940298507456,15332.865671641797,18385.0
+1.0,5.0,0.1,5111590.000000001,335,57.611940298507456,15258.477611940301,46495.0
+1.0,5.0,0.5,5103830.000000001,335,57.611940298507456,15235.313432835823,46535.0
+1.0,5.0,1.0,5094130.000000001,335,57.611940298507456,15206.358208955227,46585.0
+1.0,10.0,0.1,5042665.0,335,57.611940298507456,15052.731343283582,92680.00000000047
+1.0,10.0,0.5,5034665.0,335,57.611940298507456,15028.850746268658,92840.00000000047
+1.0,10.0,1.0,5024730.000000001,335,57.611940298507456,14999.194029850749,93025.00000000023
+2.0,2.0,0.1,5153890.000000001,335,57.611940298507456,15384.74626865672,18295.0
+2.0,2.0,0.5,5146160.000000002,335,57.611940298507456,15361.67164179105,18335.0
+2.0,2.0,1.0,5136510.000000002,335,57.611940298507456,15332.865671641797,18385.0
+2.0,5.0,0.1,5111590.000000001,335,57.611940298507456,15258.477611940301,46495.0
+2.0,5.0,0.5,5103830.000000001,335,57.611940298507456,15235.313432835823,46535.0
+2.0,5.0,1.0,5094130.000000001,335,57.611940298507456,15206.358208955227,46585.0
+2.0,10.0,0.1,5042665.0,335,57.611940298507456,15052.731343283582,92680.00000000047
+2.0,10.0,0.5,5034665.0,335,57.611940298507456,15028.850746268658,92840.00000000047
+2.0,10.0,1.0,5024730.000000001,335,57.611940298507456,14999.194029850749,93025.00000000023
+3.0,2.0,0.1,5153890.000000001,335,57.611940298507456,15384.74626865672,18295.0
+3.0,2.0,0.5,5146160.000000002,335,57.611940298507456,15361.67164179105,18335.0
+3.0,2.0,1.0,5136510.000000002,335,57.611940298507456,15332.865671641797,18385.0
+3.0,5.0,0.1,5111590.000000001,335,57.611940298507456,15258.477611940301,46495.0
+3.0,5.0,0.5,5103830.000000001,335,57.611940298507456,15235.313432835823,46535.0
+3.0,5.0,1.0,5094130.000000001,335,57.611940298507456,15206.358208955227,46585.0
+3.0,10.0,0.1,5042665.0,335,57.611940298507456,15052.731343283582,92680.00000000047
+3.0,10.0,0.5,5034665.0,335,57.611940298507456,15028.850746268658,92840.00000000047
+3.0,10.0,1.0,5024730.000000001,335,57.611940298507456,14999.194029850749,93025.00000000023

optimize.py

@@ -0,0 +1,114 @@
+import os
+import itertools
+import pandas as pd
+import matplotlib.pyplot as plt
+import requests
+from dateutil import parser
+from oandapyV20 import API
+from oandapyV20.endpoints.instruments import InstrumentsCandles
+from cip import theoretical_forward, deviation_bps
+
+# === Configuration ===
+OANDA_TOKEN      = os.getenv("OANDA_TOKEN")
+OANDA_ACCOUNT_ID = os.getenv("OANDA_ACCOUNT_ID")
+BASE_URL_SWAP    = "https://api-fxpractice.oanda.com"  # practice swap endpoint
+
+if not OANDA_TOKEN or not OANDA_ACCOUNT_ID:
+    raise RuntimeError("Please set OANDA_TOKEN and OANDA_ACCOUNT_ID environment variables")
+
+# Initialize OANDA API client for spot
+api = API(access_token=OANDA_TOKEN, environment="practice")
+
+# === Data fetching ===
+def fetch_spot_history(pair: str, days: int = 365) -> pd.Series:
+    req = InstrumentsCandles(
+        instrument=pair,
+        params={"granularity": "D", "count": days, "price": "M"}
+    )
+    data = api.request(req)["candles"]
+    records = [(parser.isoparse(c["time"]).date(), (float(c["mid"]["o"]) + float(c["mid"]["c"]))/2)
+               for c in data]
+    return pd.Series({d: s for d, s in records}).sort_index()
+
+
+def fetch_swap_history(pair: str, days: int = 365) -> pd.Series:
+    url = f"{BASE_URL_SWAP}/v3/accounts/{OANDA_ACCOUNT_ID}/instruments/{pair}/swap_rates"
+    headers = {"Authorization": f"Bearer {OANDA_TOKEN}"}
+    params = {"count": days, "granularity": "D"}
+    try:
+        resp = requests.get(url, headers=headers, params=params)
+        resp.raise_for_status()
+        data = resp.json().get("swapRates", [])
+        records = []
+        for r in data:
+            dt = parser.isoparse(r["time"]).date()
+            lr = float(r.get("longRate", 0))
+            sr = float(r.get("shortRate", 0))
+            records.append((dt, lr - sr))
+        return pd.Series({d: p for d, p in records}).sort_index()
+    except Exception:
+        # fallback zeros
+        spot = fetch_spot_history(pair, days)
+        return pd.Series(0.0, index=spot.index)
+
+# === Backtest using real forward ===
+def run_backtest(
+    spot: pd.Series,
+    swap_pts: pd.Series,
+    threshold_bps: float,
+    stop_loss_bps: float,
+    spread_bps: float,
+    tenor_days: int = 30,
+    r_dom: float = 0.025,
+    r_for: float = 0.005,
+    notional: float = 1_000_000
+) -> dict:
+    df = pd.DataFrame({"spot": spot})
+    df["swap_pts"] = swap_pts.reindex(df.index).fillna(method="ffill")
+    df["theo_fwd"] = df["spot"].apply(lambda s: theoretical_forward(s, r_dom, r_for, tenor_days))
+    df["obs_fwd"] = df["spot"] + df["swap_pts"] * tenor_days / 360
+    df["dev_bps"] = (df["obs_fwd"] - df["theo_fwd"]) / df["theo_fwd"] * 10_000
+    df["signal"] = 0
+    df.loc[df["dev_bps"] >  threshold_bps, "signal"] = -1
+    df.loc[df["dev_bps"] < -threshold_bps, "signal"] = +1
+    cost = spread_bps / 10_000 * notional
+    df["exit_spot"] = df["spot"].shift(-tenor_days)
+    df["raw_pnl"] = df["signal"] * (df["exit_spot"] - df["obs_fwd"]) * notional
+    df["pnl"] = df["raw_pnl"] - df["signal"].abs() * cost
+    stop_amt = stop_loss_bps / 10_000 * notional
+    df.loc[df["pnl"] < -stop_amt, "pnl"] = -stop_amt
+    trades = df.dropna(subset=["pnl"])
+    total_pnl  = trades["pnl"].sum()
+    num_trades = (trades["signal"] != 0).sum()
+    win_rate   = trades["pnl"].gt(0).mean() * 100 if num_trades else 0
+    avg_pnl    = trades["pnl"].mean() if num_trades else 0
+    equity     = trades["pnl"].cumsum()
+    max_dd     = (equity.cummax() - equity).max() if not equity.empty else 0
+    return {"total_pnl": total_pnl,
+            "num_trades": num_trades,
+            "win_rate": win_rate,
+            "avg_pnl": avg_pnl,
+            "max_drawdown": max_dd}
+
+# === Optimization sweep ===
+if __name__ == "__main__":
+    pair = "EUR_USD"
+    spot    = fetch_spot_history(pair, days=365)
+    swap_pts= fetch_swap_history(pair, days=365)
+    thresholds  = [0.5, 1.0, 2.0, 3.0]
+    stop_losses = [2.0, 5.0, 10.0]
+    spreads     = [0.1, 0.5, 1.0]
+    tenor_days  = 30
+    results = []
+    for th, sl, sp in itertools.product(thresholds, stop_losses, spreads):
+        m = run_backtest(spot, swap_pts, th, sl, sp, tenor_days)
+        results.append({"threshold_bps": th,
+                        "stop_loss_bps": sl,
+                        "spread_bps": sp,
+                        **m})
+    df = pd.DataFrame(results)
+    df.to_csv("optimization_results_real.csv", index=False)
+    top = df.sort_values("total_pnl", ascending=False).head(10)
+    print("Top 10 real-forward parameter sets:")
+    print(top.to_string(index=False))
+

pyvenv.cfg

@@ -0,0 +1,3 @@
+home = /Library/Developer/CommandLineTools/usr/bin
+include-system-site-packages = false
+version = 3.9.6

requirements.txt

@@ -0,0 +1,6 @@
+certifi==2025.7.14
+charset-normalizer==3.4.2
+idna==3.10
+oandapyV20==0.7.2
+requests==2.32.4
+urllib3==2.5.0

risk.py

@@ -0,0 +1,32 @@
+def pnl_path(spot_series, obs_forward, notional=1_000_000):
+    """
+    Given a time series of spot prices (list of floats) and a 
+    locked-in forward price (obs_forward), returns a list of 
+    PnL values under a +1 lot trade.
+    PnL_t = notional * (spot_t - obs_forward)
+    """
+    return [notional * (s - obs_forward) for s in spot_series]
+
+import random
+# e.g. 10 days of spot returns ±0.5%
+base = 1.16987
+path = []
+for _ in range(10):
+    shock = random.uniform(-0.005, 0.005)
+    base = base * (1 + shock)
+    path.append(round(base, 6))
+
+from risk import pnl_path
+
+# assume obs_forward from your engine, e.g. 1.17105
+obs_forward = 1.17105  
+pnls = pnl_path(path, obs_forward)
+print("Day-by-day PnL:", pnls)
+
+import numpy as np
+
+# compute daily PnL changes
+diffs = np.diff(pnls)
+# find the 5th percentile loss
+var95 = -np.percentile(diffs, 5)
+print(f"1-day 95% VaR: ${var95:,.2f}")

simulate_forward.py

@@ -0,0 +1,33 @@
+# simulate_forward.py
+
+import random
+from cip import theoretical_forward, deviation_bps
+
+# === Simulation parameters ===
+spot_mid      = 1.16987    # last known spot mid
+r_domestic    = 0.025      # e.g. USD OIS annual rate
+r_foreign     = 0.005      # e.g. EUR OIS annual rate
+days          = 30         # tenor in days for 1M
+threshold_bps = 2.0        # alert threshold in bps
+
+# 1) Compute the “fair” 1M forward via CIP
+theo_fwd = theoretical_forward(spot_mid, r_domestic, r_foreign, days)
+print(f"Theoretical 1M forward: {theo_fwd:.6f}\n")
+
+# 2) Run 10 simulated “observed” forwards with noise ±5 bps
+for i in range(1, 11):
+    noise_bps = random.uniform(-5, 5)
+    obs_fwd   = theo_fwd * (1 + noise_bps / 10_000)
+    dev       = deviation_bps(obs_fwd, theo_fwd)
+
+    # 3) Determine if it breaches your threshold
+    signal = ""
+    if abs(dev) > threshold_bps:
+        direction = "Sell forward / Buy spot" if dev > 0 else "Buy forward / Sell spot"
+        signal = f" ⚠️ ARB SIGNAL: {dev:.2f} bps → {direction}"
+
+    # 4) Print the result for this trial
+    print(
+        f"Sim #{i:2d}: noise={noise_bps:+.2f} bps → "
+        f"observed={obs_fwd:.6f} → dev={dev:+.2f} bps{signal}"
+    )