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feat: Live MT5 EAs page + parser improvements

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Live MT5 EAs page (view_ftp_tracker.py):
- FTP-based multi-account tracker replacing view_mt5_tracker.py
- Account configuration with add/remove, FTP folder validation, Demo/Personal/Prop types
- Prop account fields: profit target %, max loss %, daily loss % with EA hard stop banner
- Account summary cards: recovery factor, loss streak, stagnation days, today P&L, prop progress bars
- Calendar view: month/week/year, Mon-Fri only, weekly total column, $ or % toggle
- Open positions table parsed from MT5 HTML Open Positions section
- Symbol correlation heatmap (collapsible)
- Trade analysis section with full stats, equity/drawdown/daily P&L, DOW/hour, monthly table
- Analysis modes: Overall, By Account, By Symbol, By Algo, By Day of Week
- Dynamic combined balance field auto-updates from selected accounts
- Auto-refresh polling with session state timestamp guard
- Report date extracted from MT5 HTML header (Date: field)
- Drawdown $ / % toggle with spline smoothing

mt5_parser.py:
- calc_stats(df, deposit=0.0) — deposit-aware balance drawdown matching MT5 Balance Drawdown Maximal
- max_drawdown_pct and peak_equity added to calc_stats return
- peak_at_dd uses .loc[] fix (IndexError on filtered DataFrames)
- parse_open_positions() — parses Open Positions section from MT5 account HTML
- extract_strategy() — filters sl/tp/so close-reason comments, maps nan to Manual

ftp_sync_cli.py:
- New CLI tool for FTP connection testing and cache population

MT5Tools_FTP_Setup_Guide.docx:
- FileZilla Server setup, MT5 publisher config, CLI verification, troubleshooting
This commit is contained in:
unknown
2026-04-20 08:30:46 +10:00
parent e1b426c9b2
commit 6f8e01bcf1
13 changed files with 1834 additions and 105 deletions
Download Patch File Download Diff File Expand all files Collapse all files
view_portfolio_master.py
+59 -13
View File
@@ -33,7 +33,7 @@ def _parse_file(file_obj):
try:
parser = _get_parser()
raw = file_obj.read()
result = parser.detect_and_parse(raw)
result = parser.detect_and_parse(raw, file_obj.name)
return result[0] if isinstance(result, tuple) else result
except Exception as e:
st.error(f"Failed to parse **{file_obj.name}**: {e}")
@@ -69,6 +69,7 @@ def _normalise(df: pd.DataFrame, label: str) -> pd.DataFrame:
if "net_profit" in df.columns:
df["net_profit"] = pd.to_numeric(df["net_profit"], errors="coerce").fillna(0)
df["_strategy"] = label
df["_ea"] = label # EA = the uploaded filename stem
return df
@@ -482,6 +483,32 @@ def _init_state():
st.session_state[k] = v
def _build_strategy_dfs(file_dfs: dict) -> dict:
"""
Given {filename: df}, return {strategy_label: df} where each entry is
all trades for one unique strategy comment across all files.
Label format: "EA — Strategy" when a file has multiple strategies,
otherwise just the strategy name.
"""
result = {}
for ea_label, df in file_dfs.items():
if "strategy" not in df.columns:
result[ea_label] = df
continue
strategies = df["strategy"].dropna().unique().tolist()
if len(strategies) == 1:
# Single strategy in file — use strategy name as label
lbl = strategies[0] if strategies[0] != "Manual" else ea_label
result[lbl] = df.copy()
else:
for strat in strategies:
s_df = df[df["strategy"] == strat].copy()
if not s_df.empty:
lbl = f"{ea_label} — {strat}"
result[lbl] = s_df
return result
# ─────────────────────────────────────────────────────────────────────────────
# Render
# ─────────────────────────────────────────────────────────────────────────────
@@ -558,7 +585,9 @@ def render():
st.info("Upload backtest files above to get started.")
return
labels = list(strategy_dfs.keys())
# Explode each uploaded file into per-strategy DataFrames
all_strategy_dfs = _build_strategy_dfs(strategy_dfs)
labels = list(all_strategy_dfs.keys())
# ── Tabs ─────────────────────────────────────────────────────────────────
tab_config, tab_strategies, tab_results = st.tabs([
@@ -658,8 +687,26 @@ def render():
step=500, key="pm_mc_samples")
# ── Combination count estimate + warning ─────────────────────────────
sel_labels = st.multiselect("Strategies to include", labels,
default=labels, key="pm_sel_labels")
# ── Strategies to include ─────────────────────────────────────────────
st.markdown('<div class="sh">Strategies to Include</div>', unsafe_allow_html=True)
ea_names = list(strategy_dfs.keys())
sel_eas = st.multiselect(
"Filter by EA (file)",
ea_names, default=ea_names, key="pm_sel_eas",
help="Select which uploaded files to draw strategies from",
)
# Build strategy list cascading from EA selection
if sel_eas:
avail_strats = [lbl for lbl in labels
if any(lbl == ea or lbl.startswith(f"{ea} — ")
for ea in sel_eas)]
else:
avail_strats = labels
sel_labels = st.multiselect(
"Strategies to include",
avail_strats, default=avail_strats, key="pm_sel_labels",
help="Each strategy = one unique comment group within an EA file",
)
n_sel = len(sel_labels)
if n_sel >= int(min_strats):
@@ -766,7 +813,7 @@ def render():
else:
filtered_dfs = {}
for lbl in sel_labels:
df = strategy_dfs[lbl].copy()
df = all_strategy_dfs[lbl].copy()
if date_from and date_to and "close_time" in df.columns:
ct = pd.to_datetime(df["close_time"]).dt.tz_localize(None)
df = df[(ct >= pd.Timestamp(date_from)) &
@@ -859,7 +906,7 @@ def render():
rows = []
for i, label in enumerate(labels):
custom = st.session_state.pm_custom_names.get(label, "")
row = _full_stats(strategy_dfs[label], dep_s, i+1, custom)
row = _full_stats(all_strategy_dfs[label], dep_s, i+1, custom)
if row: rows.append(row)
if rows:
@@ -911,7 +958,7 @@ def render():
hc1, hc2 = st.columns(2)
with hc1:
st.markdown("##### Pairwise Correlation (all days)")
corr = _correlation_matrix(strategy_dfs)
corr = _correlation_matrix(all_strategy_dfs)
disp_labels = [st.session_state.pm_custom_names.get(l,l) for l in corr.columns]
corr.index = corr.columns = disp_labels
st.plotly_chart(_corr_fig(corr, height=max(300, len(labels)*55)),
@@ -919,7 +966,7 @@ def render():
with hc2:
st.markdown("##### Conditional Correlation (drawdown days only)")
dep_s2 = st.session_state.pm_deposit
cond = _conditional_correlation(strategy_dfs, dep_s2)
cond = _conditional_correlation(all_strategy_dfs, dep_s2)
cond.index = cond.columns = disp_labels
st.plotly_chart(_corr_fig(cond, height=max(300, len(labels)*55)),
use_container_width=True, key=f"pm_corr_cond_{len(labels)}")
@@ -1087,7 +1134,7 @@ def render():
# Mini equity chart
with dc1:
frames = [strategy_dfs[m].copy() for m in r["members"] if m in strategy_dfs]
frames = [all_strategy_dfs[m].copy() for m in r["members"] if m in all_strategy_dfs]
if frames:
combined = pd.concat(frames, ignore_index=True)
if "close_time" in combined.columns:
@@ -1111,7 +1158,6 @@ def render():
yaxis2=dict(overlaying="y", side="right",
gridcolor="#1E2130", tickprefix="$", showgrid=False),
)
# Add invisible annotation to ensure figure hash is unique per portfolio
pfig.add_annotation(text=str(i), x=0, y=0, opacity=0,
showarrow=False, xref="paper", yref="paper")
st.plotly_chart(pfig, use_container_width=True, key=f"pm_pfig_{i}")
@@ -1119,7 +1165,7 @@ def render():
# Per-result correlation heatmap
with dc2:
if len(r["members"]) > 1:
member_dfs = {m: strategy_dfs[m] for m in r["members"] if m in strategy_dfs}
member_dfs = {m: all_strategy_dfs[m] for m in r["members"] if m in all_strategy_dfs}
if len(member_dfs) > 1:
r_corr = _correlation_matrix(member_dfs)
r_cond = _conditional_correlation(member_dfs, st.session_state.pm_deposit)
@@ -1136,8 +1182,8 @@ def render():
# Member stats table
m_rows = []
for m in r["members"]:
if m not in strategy_dfs: continue
s = _full_stats(strategy_dfs[m], st.session_state.pm_deposit,
if m not in all_strategy_dfs: continue
s = _full_stats(all_strategy_dfs[m], st.session_state.pm_deposit,
labels.index(m)+1,
st.session_state.pm_custom_names.get(m,""))
if s: