sizing, progressionbar,reporting basics

media/basic/.gitignore

@@ -0,0 +1,5 @@
+# Ignore everything in this directory
+*
+
+# Except this file
+!.gitignore

v2realbot/common/PrescribedTradeModel.py

@@ -30,6 +30,8 @@ class Trade(BaseModel):
     entry_price: Optional[float] = None
     goal_price: Optional[float] = None
     size: Optional[int] = None
+    # size_multiplier je pomocna promenna pro pocitani relativniho denniho profit
+    size_multiplier: Optional[float] = None    
     # stoploss_type: TradeStoplossType
     stoploss_value: Optional[float] = None
     profit: Optional[float] = 0

v2realbot/config.py

@@ -1,6 +1,10 @@
 from alpaca.data.enums import DataFeed
 from v2realbot.enums.enums import Mode, Account, FillCondition
 from appdirs import user_data_dir
+from pathlib import Path
+
+#directory for generated images and basic reports
+MEDIA_DIRECTORY = Path(__file__).parent.parent / "media"
 
 #'0.0.0.0',
 #currently only prod server has acces to LIVE

v2realbot/controller/services.py

@@ -13,7 +13,7 @@ from v2realbot.utils.ilog import delete_logs
 from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus, TradeStoplossType
 from datetime import datetime
 from threading import Thread, current_thread, Event, enumerate
-from v2realbot.config import STRATVARS_UNCHANGEABLES, ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, ACCOUNT1_LIVE_API_KEY, ACCOUNT1_LIVE_SECRET_KEY, DATA_DIR,BT_FILL_CONS_TRADES_REQUIRED,BT_FILL_LOG_SURROUNDING_TRADES,BT_FILL_CONDITION_BUY_LIMIT,BT_FILL_CONDITION_SELL_LIMIT, GROUP_TRADES_WITH_TIMESTAMP_LESS_THAN
+from v2realbot.config import STRATVARS_UNCHANGEABLES, ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, ACCOUNT1_LIVE_API_KEY, ACCOUNT1_LIVE_SECRET_KEY, DATA_DIR,BT_FILL_CONS_TRADES_REQUIRED,BT_FILL_LOG_SURROUNDING_TRADES,BT_FILL_CONDITION_BUY_LIMIT,BT_FILL_CONDITION_SELL_LIMIT, GROUP_TRADES_WITH_TIMESTAMP_LESS_THAN, MEDIA_DIRECTORY
 import importlib
 from alpaca.trading.requests import GetCalendarRequest
 from alpaca.trading.client import TradingClient
@@ -35,6 +35,8 @@ import v2realbot.strategyblocks.indicators.custom as ci
 from v2realbot.strategyblocks.inits.init_indicators import initialize_dynamic_indicators
 from v2realbot.strategyblocks.indicators.indicators_hub import populate_dynamic_indicators
 from v2realbot.interfaces.backtest_interface import BacktestInterface
+import os
+from v2realbot.reporting.metricstoolsimage import generate_trading_report_image
 
 #from pyinstrument import Profiler
 #adding lock to ensure thread safety of TinyDB (in future will be migrated to proper db)
@@ -332,6 +334,12 @@ def capsule(target: object, db: object, inter_batch_params: dict = None):
                 archive_runner(runner=i, strat=target, inter_batch_params=inter_batch_params)
                 #mazeme runner po skonceni instance
                 db.runners.remove(i)
+                #vytvoreni report image pro RUNNER
+                try:
+                    generate_trading_report_image(runner_ids=[str(i.id)])
+                    print("DAILY REPORT IMAGE CREATED")
+                except Exception as e:
+                    print("Nepodarilo se vytvorit report image", str(e)+format_exc())       
 
     print("Runner STOPPED")
 
@@ -500,6 +508,13 @@ def batch_run_manager(id: UUID, runReq: RunRequest, rundays: list[RunDay]):
             #i.history += str(runner.__dict__)+"<BR>"
             db.save()
 
+    #vytvoreni report image pro batch
+    try:
+        generate_trading_report_image(batch_id=batch_id)
+        print("BATCH REPORT IMAGE CREATED")
+    except Exception as e:
+        print("Nepodarilo se vytvorit report image", str(e)+format_exc())       
+
 #stratin run
 def run_stratin(id: UUID, runReq: RunRequest, synchronous: bool = False, inter_batch_params: dict = None):
     if runReq.mode == Mode.BT:
@@ -676,12 +691,6 @@ def populate_metrics_output_directory(strat: StrategyInstance, inter_batch_param
         res["profit"]["batch_sum_profit"] = int(inter_batch_params["batch_profit"])
         res["profit"]["batch_sum_rel_profit"] = inter_batch_params["batch_rel_profit"]
 
-    #rel_profit zprumerovane
-    res["profit"]["daily_rel_profit_avg"] = float(np.sum(strat.state.rel_profit_cum)) if len(strat.state.rel_profit_cum) > 0 else 0
-    #rel_profit rozepsane zisky
-    res["profit"]["daily_rel_profit_list"] = strat.state.rel_profit_cum
-
-
     #metrikz z prescribedTrades, pokud existuji
     try:
         long_profit = 0
@@ -696,39 +705,53 @@ def populate_metrics_output_directory(strat: StrategyInstance, inter_batch_param
         max_loss_time = None
         long_cnt = 0
         short_cnt = 0
+        sum_wins_profit= 0
+        sum_loss = 0
 
         if "prescribedTrades" in strat.state.vars:
             for trade in strat.state.vars.prescribedTrades:
-                if trade.profit_sum < max_loss:
-                    max_loss = trade.profit_sum
-                    max_loss_time = trade.last_update
-                if trade.profit_sum > max_profit:
-                    max_profit = trade.profit_sum
-                    max_profit_time = trade.last_update
-                if trade.status == TradeStatus.ACTIVATED and trade.direction == TradeDirection.LONG:
-                    long_cnt += 1
-                    if trade.profit is not None:
-                        long_profit += trade.profit
-                        if trade.profit < 0:
-                            long_losses += trade.profit
-                        if trade.profit > 0:
-                            long_wins += trade.profit
-                if trade.status == TradeStatus.ACTIVATED and trade.direction == TradeDirection.SHORT:
-                    short_cnt +=1
-                    if trade.profit is not None:
-                        short_profit += trade.profit
-                        if trade.profit < 0:
-                            short_losses += trade.profit
-                        if trade.profit > 0:
-                            short_wins += trade.profit
+                if trade.status == TradeStatus.CLOSED:
+                    if trade.profit_sum < max_loss:
+                        max_loss = trade.profit_sum
+                        max_loss_time = trade.last_update
+                    if trade.profit_sum > max_profit:
+                        max_profit = trade.profit_sum
+                        max_profit_time = trade.last_update
+                    if trade.direction == TradeDirection.LONG:
+                        long_cnt += 1
+                        if trade.profit is not None:
+                            long_profit += trade.profit
+                            if trade.profit < 0:
+                                long_losses += trade.profit
+                            if trade.profit > 0:
+                                long_wins += trade.profit
+                    if trade.direction == TradeDirection.SHORT:
+                        short_cnt +=1
+                        if trade.profit is not None:
+                            short_profit += trade.profit
+                            if trade.profit < 0:
+                                short_losses += trade.profit
+                            if trade.profit > 0:
+                                short_wins += trade.profit
+            sum_wins = long_wins + short_wins
+            sum_losses = long_losses + short_losses
+            #toto nejak narovnat, mozna diskutovat s Martinem nebo s Vercou
+
+            #zatim to neukazuje moc jasne - poznámka: ztráta by měla být jenom negativní profit, nikoliv nová veličina
+            #jediná vyjímka je u max.kumulativní ztráty (drawdown)
+            res["profit"]["sum_wins"] = sum_wins
+            res["profit"]["sum_losses"] = sum_losses
             res["profit"]["long_cnt"] = long_cnt
-            res["profit"]["short_cnt"] = short_cnt          
+            res["profit"]["short_cnt"] = short_cnt
+            #celkovy profit za long/short          
             res["profit"]["long_profit"] = round(long_profit,2)
             res["profit"]["short_profit"] = round(short_profit,2)
-            res["profit"]["max_profit"] = round(max_profit,2)
-            res["profit"]["max_profit_time"] = str(max_profit_time)
-            res["profit"]["max_loss"] = round(max_loss,2)
-            res["profit"]["max_loss_time"] = str(max_loss_time)
+            #maximalni kumulativni profit (tzn. peaky profitu)
+            res["profit"]["max_profit_cum"] = round(max_profit,2)
+            res["profit"]["max_profit_cum_time"] = str(max_profit_time)
+            #maximalni kumulativni ztrata (tzn. peaky v lossu)
+            res["profit"]["max_loss_cum"] = round(max_loss,2)
+            res["profit"]["max_loss_time_cum"] = str(max_loss_time)
             res["profit"]["long_wins"] = round(long_wins,2)
             res["profit"]["long_losses"] = round(long_losses,2)
             res["profit"]["short_wins"] = round(short_wins,2)
@@ -739,7 +762,13 @@ def populate_metrics_output_directory(strat: StrategyInstance, inter_batch_param
             rp_string = "RP" + str(float(np.sum(strat.state.rel_profit_cum))) if len(strat.state.rel_profit_cum) >0 else "noRP"
 
             ##summary pro rychle zobrazeni P333L-222 PT9:30 PL10:30
-            res["profit"]["sum"]="P"+str(int(max_profit))+"L"+str(int(max_loss))+" "+ mpt_string+" " + mlt_string + rp_string + " "+str(strat.state.rel_profit_cum)
+            res["profit"]["sum"]="P"+str(int(sum_wins))+"L"+str(int(sum_losses))+" "+"MCP"+str(int(max_profit))+"MCL(DD)"+str(int(max_loss))+" "+ mpt_string+" " + mlt_string + rp_string + " "+str(strat.state.rel_profit_cum)
+
+            #rel_profit zprumerovane
+            res["profit"]["daily_rel_profit_sum"] = float(np.sum(strat.state.rel_profit_cum)) if len(strat.state.rel_profit_cum) > 0 else 0
+            #rel_profit rozepsane zisky
+            res["profit"]["daily_rel_profit_list"] = strat.state.rel_profit_cum
+
             #vlozeni celeho listu
             res["prescr_trades"]=json.loads(json.dumps(strat.state.vars.prescribedTrades, default=json_serial))
 
@@ -1007,6 +1036,25 @@ def edit_archived_runners(runner_id: UUID, archChange: RunArchiveChange):
         print(errmsg)
         return -2, errmsg
 
+
+def delete_report_files(id):
+    
+    #ZATIM MAME JEN BASIC
+    #delete report images
+    image_file_name = f"{id}.png"
+    image_path = str(MEDIA_DIRECTORY / "basic" / image_file_name)
+    try:
+        if os.path.exists(image_path):
+            os.remove(image_path)
+            print(f"File {image_path} has been deleted.")
+            return (0, "deleted")
+        else:
+            print(f"No File {image_path} found to delte.")
+            return (1, "not found")
+    except Exception as e:
+        print(f"An error occurred while deleting the file: {e}")
+        return (-1, str(e))        
+
 #delete runner in archive and archive detail and runner logs
 #predelano do JEDNE TRANSAKCE
 def delete_archived_runners_byIDs(ids: list[UUID]):
@@ -1016,6 +1064,19 @@ def delete_archived_runners_byIDs(ids: list[UUID]):
         for id in ids:
             c = conn.cursor()
             print(str(id))
+
+           # Get batch_id for the current runner_id
+            c.execute("SELECT batch_id FROM runner_header WHERE runner_id = ?", (str(id),))
+            batch_id = c.fetchone()
+            if batch_id:
+                batch_id = batch_id[0]
+                # Check if this is the last record with the given batch_id
+                c.execute("SELECT COUNT(*) FROM runner_header WHERE batch_id = ?", (batch_id,))
+                count = c.fetchone()[0]
+                if count == 1:
+                    # If it's the last record, call delete_report_files
+                    delete_report_files(batch_id)
+
             resh = c.execute(f"DELETE from runner_header WHERE runner_id='{str(id)}';")
             print("header deleted",resh.rowcount)
             resd = c.execute(f"DELETE from runner_detail WHERE runner_id='{str(id)}';")
@@ -1025,6 +1086,9 @@ def delete_archived_runners_byIDs(ids: list[UUID]):
             out.append(str(id) + ":   " + str(resh.rowcount) + " " + str(resd.rowcount) + " " + str(resl.rowcount))
             conn.commit()
             print("commit")
+
+            delete_report_files(id)
+
         # if resh.rowcount == 0 or resd.rowcount == 0:
         #     return -1, "not found "+str(resh.rowcount) + " " + str(resd.rowcount) + " " + str(resl.rowcount)
         return 0, out
@@ -1044,6 +1108,15 @@ def delete_archive_header_byID(id: UUID):
         res = execute_with_retry(c,statement)
         conn.commit()
         print("deleted", res.rowcount)
+        #delete report images
+        image_file_name = f"report_{id}.png"
+        image_path = str(MEDIA_DIRECTORY / image_file_name)
+        try:
+            if os.path.exists(image_path):
+                os.remove(image_path)
+                print(f"File {image_path} has been deleted.")
+        except Exception as e:
+            print(f"An error occurred while deleting the file: {e}")  
     finally:
         pool.release_connection(conn)
     return res.rowcount

v2realbot/loader/trade_offline_streamer.py

@@ -8,7 +8,7 @@ from alpaca.data.enums import DataFeed
 from alpaca.data.historical import StockHistoricalDataClient
 from alpaca.data.requests import StockLatestQuoteRequest, StockBarsRequest, StockTradesRequest
 from threading import Thread, current_thread
-from v2realbot.utils.utils import parse_alpaca_timestamp, ltp, zoneNY, print
+from v2realbot.utils.utils import parse_alpaca_timestamp, ltp, zoneNY
 from v2realbot.utils.tlog import tlog
 from datetime import datetime, timedelta, date
 from threading import Thread
@@ -21,6 +21,8 @@ import os
 from rich import print
 import queue
 from alpaca.trading.models import Calendar
+from tqdm import tqdm
+
 """
     Trade offline data streamer, based on Alpaca historical data.
 """
@@ -212,7 +214,7 @@ class Trade_Offline_Streamer(Thread):
                 cnt = 1
                 
                 
-                for t in tradesResponse[symbol]:
+                for t in tqdm(tradesResponse[symbol]):
                     
                     #protoze je zde cely den, poustime dal, jen ty relevantni
                     #pokud je    start_time < trade < end_time

v2realbot/main.py

@@ -1,6 +1,6 @@
 import os,sys
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from v2realbot.config import WEB_API_KEY, DATA_DIR
+from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY
 from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
 from datetime import datetime
 import os
@@ -13,7 +13,7 @@ import v2realbot.controller.services as cs
 from v2realbot.utils.ilog import get_log_window
 from v2realbot.common.model import StrategyInstance, RunnerView, RunRequest, Trade, RunArchive, RunArchiveView, RunArchiveDetail, Bar, RunArchiveChange, TestList, ConfigItem, InstantIndicator
 from fastapi import FastAPI, WebSocket, WebSocketDisconnect, Depends, HTTPException, status, WebSocketException, Cookie, Query
-from fastapi.responses import FileResponse
+from fastapi.responses import FileResponse, StreamingResponse
 from fastapi.staticfiles import StaticFiles
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from v2realbot.enums.enums import Env, Mode
@@ -30,6 +30,9 @@ from v2realbot.utils.sysutils import get_environment
 from uuid import uuid4
 from sqlite3 import OperationalError
 from time import sleep
+import v2realbot.reporting.metricstools as mt
+from v2realbot.reporting.metricstoolsimage import generate_trading_report_image
+from traceback import format_exc
 #from async io import Queue, QueueEmpty
                    
 # install()
@@ -64,6 +67,7 @@ def api_key_auth(api_key: str = Depends(X_API_KEY)):
 app = FastAPI()
 root = os.path.dirname(os.path.abspath(__file__))
 app.mount("/static", StaticFiles(html=True, directory=os.path.join(root, 'static')), name="static")
+app.mount("/media", StaticFiles(directory=str(MEDIA_DIRECTORY)), name="media")
 #app.mount("/", StaticFiles(html=True, directory=os.path.join(root, 'static')), name="www")
 
 security = HTTPBasic()
@@ -459,7 +463,6 @@ def _delete_indicator_byName(runner_id: UUID, indicator: InstantIndicator):
         raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f"Error not changed: {res}:{runner_id}:{vals}")
 
 
-
 #edit archived runner ("note",..)
 @app.patch("/archived_runners/{runner_id}", dependencies=[Depends(api_key_auth)])
 def _edit_archived_runners(archChange: RunArchiveChange, runner_id: UUID):
@@ -509,6 +512,31 @@ def _get_alpaca_history_bars(symbol: str, datetime_object_from: datetime, dateti
     else:
         raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"No data found {res} {set}")
 
+#get pdf report - WIP
+@app.put("/archived_runners/{runner_id}/generatepdf", dependencies=[Depends(api_key_auth)], responses={200: {"content": {"application/pdf": {}}}})
+def _generat_pdf(runner_id: UUID):
+    #jako vstup umouznit i seznam runneru - vytvori to pote report ze vsech techto
+    #pripadne mit jako vstup batch a udelat to pro batch ()
+    res, vals = mt.create_trading_report_pdf(id=runner_id)
+    if res == 0:
+        # Return the PDF data as a streaming response {str(runner_id)}
+        return StreamingResponse(vals, media_type="application/pdf", headers={"Content-Disposition": "attachment; filename=report.pdf"})
+    elif res == -1:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Error no runner: {runner_id} {res}:{vals}")
+    else:
+        raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f"Error not changed: {res}:{runner_id}:{vals}")
+
+#generate image based list of ids
+@app.post("/archived_runners/generatereportimage", dependencies=[Depends(api_key_auth)], responses={200: {"content": {"image/png": {}}}})
+def _generate_report_image(runner_ids: list[UUID]):
+    try:
+        res, stream = generate_trading_report_image(runner_ids=runner_ids,stream=True)
+        if res == 0: return StreamingResponse(stream, media_type="image/png",headers={"Content-Disposition": "attachment; filename=report.png"})
+        elif res < 0:
+            raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f"Error: {res}:{id}")
+    except Exception as e:
+            raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f"Error: {str(e)}" + format_exc())        
+
 #TestList APIS - do budoucna predelat SQL do separatnich funkci
 @app.post('/testlists/', dependencies=[Depends(api_key_auth)])
 def create_record(testlist: TestList):

v2realbot/reporting/metricstools.py

@@ -0,0 +1,145 @@
+import json
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')  # Set the Matplotlib backend to 'Agg'
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+from fpdf import FPDF, XPos, YPos
+from datetime import datetime
+from io import BytesIO
+import v2realbot.controller.services as cs
+from rich import print
+def create_trading_report_pdf(id, direct = True, output_file='trading_report.pdf'):
+    
+    #get runner
+    res, set =cs.get_archived_runner_header_byID(id)
+    if res != 0:
+        return -1, f"no runner {id} found"
+    
+    print("archrunner")
+    print(set)
+    
+    # Parse JSON data
+    data = set.metrics
+    profit_data = data["profit"]
+    pos_cnt_data = data["pos_cnt"]
+    prescr_trades_data = data["prescr_trades"]
+
+    # PDF setup
+    pdf = FPDF()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.set_font("Helvetica", size=10)
+
+    # Start the first page for plots
+    pdf.add_page()
+
+    # Create a combined figure for all plots (adjusting the layout to 3x3)
+    fig, axs = plt.subplots(3, 3, figsize=(15, 15))
+
+    # Plot 1: Overall Profit Summary Chart
+    sns.barplot(x=['Total Wins', 'Total Losses', 'Net Profit'],
+                y=[profit_data["sum_wins"], profit_data["sum_losses"],
+                   profit_data["sum_wins"] - profit_data["sum_losses"]],
+                ax=axs[0, 0])
+    axs[0, 0].set_title('Overall Profit Summary')
+
+    # Plot 2: Profit Distribution by Trade Type
+    axs[0, 1].pie([profit_data["long_profit"], profit_data["short_profit"]],
+                  labels=['Long Profit', 'Short Profit'], autopct='%1.1f%%')
+    axs[0, 1].set_title('Profit Distribution by Trade Type')
+
+    # Plot 3: Cumulative Profit Over Time Line Chart
+    exit_times = [datetime.fromtimestamp(trade["exit_time"]) for trade in prescr_trades_data]
+    cumulative_profits = [trade["profit_sum"] for trade in prescr_trades_data]
+    sns.lineplot(x=exit_times, y=cumulative_profits, ax=axs[0, 2])
+    axs[0, 2].set_title('Cumulative Profit Over Time')
+    axs[0, 2].tick_params(axis='x', rotation=45)
+
+    # Plot 4: Cumulative Profit Over Time with Max Profit Point
+    sns.lineplot(x=exit_times, y=cumulative_profits, label='Cumulative Profit', ax=axs[1, 0])
+    max_profit_time = datetime.fromisoformat(profit_data["max_profit_cum_time"])
+    max_profit = profit_data["max_profit_cum"]
+    axs[1, 0].scatter(max_profit_time, max_profit, color='green', label='Max Profit')
+    axs[1, 0].set_title('Cumulative Profit Over Time with Max Profit Point')
+    axs[1, 0].tick_params(axis='x', rotation=45)
+    axs[1, 0].legend()
+
+    # Plot 5: Trade Counts Bar Chart
+    sns.barplot(x=['Long Trades', 'Short Trades'],
+                y=[profit_data["long_cnt"], profit_data["short_cnt"]],
+                ax=axs[1, 1])
+    axs[1, 1].set_title('Trade Counts')
+
+    # Plot 6: Position Size Distribution
+    sns.barplot(x=list(pos_cnt_data.keys()), y=list(pos_cnt_data.values()), ax=axs[1, 2])
+    axs[1, 2].set_title('Position Size Distribution')
+
+    # Plot 7: Daily Relative Profit Chart
+    sns.lineplot(x=range(len(profit_data["daily_rel_profit_list"])), y=profit_data["daily_rel_profit_list"], ax=axs[2, 0])
+    axs[2, 0].set_title('Daily Relative Profit')
+    axs[2, 0].set_xlabel('Trade Number')
+    axs[2, 0].set_ylabel('Relative Profit')
+
+    # Adjust layout, save the combined plot, and add it to the PDF
+    # plt.tight_layout()
+    # plt.savefig("combined_plot.png", format="png", bbox_inches="tight")
+    # plt.close()
+    # pdf.image("combined_plot.png", x=10, y=20, w=180)
+
+    plt.tight_layout()
+    plot_buffer = BytesIO()
+    plt.savefig(plot_buffer, format="png")
+    plt.close()
+    plot_buffer.seek(0)
+    pdf.image(plot_buffer, x=10, y=20, w=180)
+    plot_buffer.close()
+
+    # Start a new page for the table and additional information
+    pdf.add_page()
+
+    # 8. Individual Trade Details Table
+    pdf.set_font("Helvetica", size=8)
+    trade_fields = ['id', 'direction', 'entry_time', 'exit_time', 'profit', 'profit_sum', 'rel_profit']
+    trades_table_data = [{field: trade[field] for field in trade_fields} for trade in prescr_trades_data]
+    trades_table = pd.DataFrame(trades_table_data)
+    for row in trades_table.values:
+        for cell in row:
+            pdf.cell(40, 10, str(cell), border=1)
+        pdf.ln()
+
+    # Profit/Loss Ratio and Relative Profit Metrics
+    profit_loss_ratio = "N/A" if profit_data["sum_losses"] == 0 else str(profit_data["sum_wins"] / profit_data["sum_losses"])
+    relative_profit = profit_data["daily_rel_profit_sum"]
+    pdf.cell(0, 10, f"Profit/Loss Ratio: {profit_loss_ratio}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+    pdf.cell(0, 10, f"Total Relative Profit: {relative_profit}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+
+    # Summary of Key Metrics
+    pdf.cell(0, 10, "\nSummary of Key Metrics:", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+    pdf.cell(0, 10, f"Total Number of Trades: {profit_data['long_cnt'] + profit_data['short_cnt']}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+    pdf.cell(0, 10, f"Total Profit: {profit_data['sum_wins']}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+    pdf.cell(0, 10, f"Total Loss: {profit_data['sum_losses']}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+    best_trade_profit = max(profit_data["long_wins"], profit_data["short_wins"])
+    pdf.cell(0, 10, f"Best Trade Profit: {best_trade_profit}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+    worst_trade_profit = min(trade["profit"] for trade in prescr_trades_data)
+    pdf.cell(0, 10, f"Worst Trade Profit: {worst_trade_profit}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+
+    # Save PDF
+    pdf.output(output_file)
+
+    if direct is False:
+        # Save PDF
+        pdf.output(output_file)
+    else:
+        # Instead of saving to a file, write to a BytesIO buffer
+        pdf_buffer = BytesIO()
+        pdf.output(pdf_buffer)
+        pdf_buffer.seek(0)  # Move to the beginning of the BytesIO buffer
+        return 0, pdf_buffer
+
+# Example usage:
+if __name__ == '__main__':
+    id = "c3e31cb5-ddf9-467e-a932-2118f6844355"
+    res, val = create_trading_report_pdf(id, True)
+
+    print(res,val)

v2realbot/reporting/metricstoolsimage.py

@@ -0,0 +1,382 @@
+import matplotlib
+import matplotlib.dates as mdates
+matplotlib.use('Agg')  # Set the Matplotlib backend to 'Agg'
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+from datetime import datetime
+from typing import List
+from enum import Enum
+import numpy as np
+import v2realbot.controller.services as cs
+from rich import print
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get, print
+from pathlib import Path
+from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY
+from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, OrderSide
+from io import BytesIO
+# Assuming Trade, TradeStatus, TradeDirection, TradeStoplossType classes are defined elsewhere
+
+def generate_trading_report_image(runner_ids: list = None, batch_id: str = None, stream: bool = False):
+    
+    #TODO dopracovat drawdown a minimalni a maximalni profity nikoliv cumulovane, zamyslet se
+    #TODO list of runner_ids
+    #TODO pridelat na vytvoreni runnera a batche, samostatne REST API + na remove archrunnera
+    
+    if runner_ids is None and batch_id is None:
+        return -2, f"runner_id or batch_id must be present"
+
+    if batch_id is not None:
+        res, runner_ids =cs.get_archived_runnerslist_byBatchID(batch_id)
+
+        if res != 0:
+            print(f"no batch {batch_id} found")
+            return -1, f"no batch {batch_id} found"
+        
+    trades = []
+    for id in runner_ids:
+        #get runner
+        res, sada =cs.get_archived_runner_header_byID(id)
+        if res != 0:
+            print(f"no runner {id} found")
+            return -1, f"no runner {id} found"
+        
+        print("archrunner")
+        print(sada)
+    
+        # Parse trades
+        #trades = [Trade(**trade_dict) for trade_dict in set.metrics["prescr_trades"]]
+
+        trades_dicts =  sada.metrics["prescr_trades"]
+
+        for trade_dict in trades_dicts:
+            trade_dict['last_update'] = datetime.fromtimestamp(trade_dict.get('last_update')).astimezone(zoneNY)
+            trade_dict['entry_time'] = datetime.fromtimestamp(trade_dict.get('entry_time')).astimezone(zoneNY)
+            trade_dict['exit_time'] = datetime.fromtimestamp(trade_dict.get('exit_time')).astimezone(zoneNY)
+            trades.append(Trade(**trade_dict))
+
+        print(trades)
+
+    # Filter to only use trades with status 'CLOSED'
+    closed_trades = [trade for trade in trades if trade.status == TradeStatus.CLOSED]
+
+    # Data extraction for the plots
+    exit_times = [trade.exit_time for trade in closed_trades if trade.exit_time is not None]
+    cumulative_profits = [trade.profit_sum for trade in closed_trades if trade.profit_sum is not None]
+    profits = [trade.profit for trade in closed_trades if trade.profit is not None]
+    wins = [trade.profit for trade in closed_trades if trade.profit > 0]
+    losses = [trade.profit for trade in closed_trades if trade.profit < 0]
+
+    wins_long = [trade.profit for trade in closed_trades if trade.profit > 0 and trade.direction == TradeDirection.LONG]
+    losses_long = [trade.profit for trade in closed_trades if trade.profit < 0 and trade.direction == TradeDirection.LONG]    
+    wins_short = [trade.profit for trade in closed_trades if trade.profit > 0 and trade.direction == TradeDirection.SHORT]
+    losses_short = [trade.profit for trade in closed_trades if trade.profit < 0 and trade.direction == TradeDirection.SHORT]    
+
+    directions = [trade.direction for trade in closed_trades]
+
+    long_profits = [trade.profit for trade in closed_trades if trade.direction == TradeDirection.LONG and trade.profit is not None]
+    short_profits = [trade.profit for trade in closed_trades if trade.direction == TradeDirection.SHORT and trade.profit is not None]
+
+    # Setting up dark mode for the plots
+    plt.style.use('dark_background')
+
+    # Optionally, you can further customize colors, labels, and axes
+    params = {
+        'axes.titlesize': 9,
+        'axes.labelsize': 8,
+        'xtick.labelsize': 9,
+        'ytick.labelsize': 9,
+        'axes.labelcolor': '#a9a9a9', #a1a3aa',
+        'axes.facecolor': '#121722', #'#0e0e0e', #202020',  # Dark background for plot area
+        'axes.grid': False,  # Turn off the grid globally
+        'grid.color': 'gray',  # If the grid is on, set grid line color
+        'grid.linestyle': '--',  # Grid line style        
+        'grid.linewidth': 1,
+        'xtick.color': '#a9a9a9',
+        'ytick.color': '#a9a9a9',
+        'axes.edgecolor': '#a9a9a9'
+    }
+    plt.rcParams.update(params)
+
+    # Create a combined figure for all plots
+    fig, axs = plt.subplots(3, 4, figsize=(11, 7))
+
+    #TITLE
+    title = ""
+    cnt_ids = len(runner_ids)
+    if batch_id is not None:
+        title = "Batch: "+str(batch_id)+ " "
+
+    title += "Days: " + str(cnt_ids)
+    if cnt_ids == 1:
+        title += " ("+str(runner_ids[0])[0:14]+") "
+       
+        if sada.mode == Mode.BT:
+            datum = sada.bt_from
+        else:
+            datum = sada.started
+
+        title += datum.strftime("%d.%m.%Y %H:%M")
+
+
+    # Add a title to the figure
+    fig.suptitle(title, fontsize=15, color='white')
+
+    # Plot 1: Overall Profit Summary Chart
+    total_wins = int(sum(wins))
+    total_losses = int(sum(losses))
+    net_profit = int(sum(profits))
+    sns.barplot(x=['Total', 'Wins','Losses'],
+                y=[net_profit, total_wins, total_losses],
+                ax=axs[0, 0])
+    axs[0, 0].set_title('Overall Profit Summary')
+    # Define the offset for placing text inside the bars
+    offset = max(total_wins, abs(total_losses), net_profit) * 0.05  # 5% of the highest (or lowest) bar value
+
+    # Function to place text annotation
+    def place_annotation(ax, x, value, offset):
+        va = 'top' if value >= 0 else 'bottom'
+        y = value - offset if value >= 0 else value + offset
+        ax.text(x, y, f'{value}', ha='center', va=va, color='black', fontsize=12)
+
+    # Annotate the Total Wins, Losses, and Net Profit bars
+    place_annotation(axs[0, 0], 0, net_profit, offset)
+    place_annotation(axs[0, 0], 1, total_wins, offset)
+    place_annotation(axs[0, 0], 2, total_losses, offset)
+
+    # Plot 2: LONG - profit summary
+    total_wins_long = int(sum(wins_long))
+    total_losses_long = int(sum(losses_long))
+    total_long = total_wins_long + total_losses_long
+    sns.barplot(x=['Total', 'Wins','Losses'],
+                y=[total_long, total_wins_long, total_losses_long],
+                ax=axs[0, 1])
+    axs[0, 1].set_title('LONG Profit Summary')
+    # Define the offset for placing text inside the bars
+    offset = max(total_wins_long, abs(total_losses_long)) * 0.05  # 5% of the highest (or lowest) bar value
+
+    place_annotation(axs[0, 1], 0, total_long, offset)
+    place_annotation(axs[0, 1], 1, total_wins_long, offset)
+    place_annotation(axs[0, 1], 2, total_losses_long, offset)
+
+
+    # Plot 3: SHORT - profit summary
+    total_wins_short =int(sum(wins_short))
+    total_losses_short = int(sum(losses_short))  
+    total_short = total_wins_short + total_losses_short
+    sns.barplot(x=['Total', 'Wins', 'Losses'],
+                y=[total_short, total_wins_short,
+                   total_losses_short],
+                ax=axs[0, 2])
+    axs[0, 2].set_title('SHORT Profit Summary')
+    # Define the offset for placing text inside the bars
+    offset = max(total_wins_short, abs(total_losses_short)) * 0.05  # 5% of the highest (or lowest) bar value
+
+    place_annotation(axs[0, 2], 0, total_short, offset)
+    place_annotation(axs[0, 2], 1, total_wins_short, offset)
+    place_annotation(axs[0, 2], 2, total_losses_short, offset)
+
+    # Plot 4: Trade Counts Bar Chart
+    long_count = len([trade for trade in closed_trades if trade.direction == TradeDirection.LONG])
+    short_count = len([trade for trade in closed_trades if trade.direction == TradeDirection.SHORT])
+    sns.barplot(x=['Long Trades', 'Short Trades'], y=[long_count, short_count], ax=axs[0, 3])
+    axs[0, 3].set_title('Trade Counts')
+    offset = max(long_count, short_count) * 0.05  # 5% of the highest (or lowest) bar value
+
+    place_annotation(axs[0, 3], 0, long_count, offset)
+    place_annotation(axs[0, 3], 1, short_count, offset)
+
+
+    #Cumulative profit - bud 1 den nebo vice dni
+    if len(runner_ids)== 1:
+        # Plot 3: Cumulative Profit Over Time with Max Profit Point
+        max_profit_time = exit_times[np.argmax(cumulative_profits)]
+        max_profit = max(cumulative_profits)
+        min_profit_time = exit_times[np.argmin(cumulative_profits)]
+        min_profit = min(cumulative_profits)
+        sns.lineplot(x=exit_times, y=cumulative_profits, label='Cumulative Profit', ax=axs[1, 3])
+        axs[1, 3].scatter(max_profit_time, max_profit, color='green', label='Max Profit')
+        axs[1, 3].scatter(min_profit_time, min_profit, color='red', label='Min Profit')
+        # Format dates on the x-axis
+        axs[1, 3].xaxis.set_major_formatter(mdates.DateFormatter('%H', tz=zoneNY))
+        axs[1, 3].set_title('Cumulative Profit Over Time')
+        axs[1, 3].legend()
+    else:
+        # Calculate cumulative profit
+        # Additional Plot: Cumulative Profit Over Time
+        # Sort trades by exit time
+        sorted_trades = sorted([trade for trade in trades if trade.status == TradeStatus.CLOSED], 
+                            key=lambda x: x.exit_time)
+        cumulative_profits = np.cumsum([trade.profit for trade in sorted_trades])
+        exit_times_sorted = [trade.exit_time for trade in sorted_trades]
+        axs[1, 3].plot(exit_times_sorted, cumulative_profits, color='blue')
+        axs[1, 3].set_title('Cumulative Profit Over Time')
+        axs[1, 3].set_xlabel('Time')
+        axs[1, 3].set_ylabel('Cumulative Profit')
+        axs[1, 3].xaxis.set_major_formatter(mdates.DateFormatter('%d', tz=zoneNY))
+
+    # Creating a DataFrame for the heatmap
+    heatmap_data_list = []
+    for trade in trades:
+        if trade.status == TradeStatus.CLOSED:
+            day = trade.exit_time.strftime('%m-%d')  # Format date as 'MM-DD'
+            #day = trade.exit_time.date()
+            hour = trade.exit_time.hour
+            profit = trade.profit
+            heatmap_data_list.append({'Day': day, 'Hour': hour, 'Profit': profit})
+
+    heatmap_data = pd.DataFrame(heatmap_data_list)
+    heatmap_data = heatmap_data.groupby(['Day', 'Hour']).sum().reset_index()
+    heatmap_pivot = heatmap_data.pivot(index='Day', columns='Hour', values='Profit')
+
+    # Plot 3: Heatmap of Profits
+    sns.heatmap(heatmap_pivot, cmap='viridis', ax=axs[1, 0])
+    axs[1, 0].set_title('Heatmap of Profits (based on Exit time)')
+    axs[1, 0].set_xlabel('Hour of Day')
+    axs[1, 0].set_ylabel('Day')
+    
+    # Plot 9: Profit/Loss Distribution Histogram
+    sns.histplot(profits, bins=30, ax=axs[1, 1], kde=True, color='skyblue')
+    axs[1, 1].set_title('Profit/Loss Distribution')
+    axs[1, 1].set_xlabel('Profit/Loss')
+    axs[1, 1].set_ylabel('Frequency')
+
+    # Plot 5
+    #    - pro 1 den: Position Size Distribution   
+    #    - pro vice dnu:  Trade Duration vs. Profit/Loss
+    if len(runner_ids) == 1:
+        
+        sizes = [trade.size for trade in closed_trades if trade.size is not None]
+        size_counts = {size: sizes.count(size) for size in set(sizes)}
+        sns.barplot(x=list(size_counts.keys()), y=list(size_counts.values()), ax=axs[1, 2])
+        axs[1, 2].set_title('Position Size Distribution')
+    else:
+        trade_durations = []
+        trade_profits = []
+        #trade_volumes = []  # Assuming you have a way to measure the size/volume of each trade
+        trade_types = []  # 'Long' or 'Short'
+
+        for trade in trades:
+            if trade.status == TradeStatus.CLOSED:
+                duration = (trade.exit_time - trade.entry_time).total_seconds() / 60  # Duration in minutes  (3600 for hours)
+                trade_durations.append(duration)
+                trade_profits.append(trade.profit)
+                ##trade_volumes.append(trade.size)  # or any other measure of trade size
+                trade_types.append('Long' if trade.direction == TradeDirection.LONG else 'Short')
+
+        # Plot 8: Trade Duration vs. Profit/Loss
+        scatter_data = pd.DataFrame({
+            'Duration': trade_durations,
+            'Profit': trade_profits,
+            #'Volume': trade_volumes,
+            'Type': trade_types
+        })
+        #sns.scatterplot(data=scatter_data, x='Duration', y='Profit', size='Volume', hue='Type', ax=axs[1, 2])
+        sns.scatterplot(data=scatter_data, x='Duration', y='Profit', hue='Type', ax=axs[1, 2])
+        axs[1, 2].set_title('Trade Duration vs. Profit/Loss')
+        axs[1, 2].set_xlabel('Duration (Minutes)')
+        axs[1, 2].set_ylabel('Profit/Loss')
+
+
+    # Plot 6: Daily Relative Profit Chart
+    if len(runner_ids) == 1:
+        daily_rel_profits = [trade.rel_profit for trade in closed_trades if trade.rel_profit is not None]
+        sns.lineplot(x=range(len(daily_rel_profits)), y=daily_rel_profits, ax=axs[2, 0])
+        axs[2, 0].set_title('Daily Relative Profit')
+    else:
+        # Creating a DataFrame for the heatmap
+        heatmap_data_list = []
+        for trade in trades:
+            if trade.status == TradeStatus.CLOSED:
+                day = trade.entry_time.strftime('%m-%d')  # Format date as 'MM-DD'
+                #day = trade.entry_time.date()
+                hour = trade.entry_time.hour
+                profit = trade.profit
+                heatmap_data_list.append({'Day': day, 'Hour': hour, 'Profit': profit})
+
+        heatmap_data = pd.DataFrame(heatmap_data_list)
+        heatmap_data = heatmap_data.groupby(['Day', 'Hour']).sum().reset_index()
+        heatmap_pivot = heatmap_data.pivot(index='Day', columns='Hour', values='Profit')
+
+        # Plot 3: Heatmap of Profits
+        sns.heatmap(heatmap_pivot, cmap='viridis', ax=axs[2, 0])
+        axs[2, 0].set_title('Heatmap of Profits (based on Entry time)')
+        axs[2, 0].set_xlabel('Hour of Day')
+        axs[2, 0].set_ylabel('Day')
+
+    # Plot 8: Profits Based on Hour of the Day (Entry)
+    entry_hours = [trade.entry_time.hour for trade in closed_trades if trade.entry_time is not None]
+    profits_by_hour = {}
+    for hour, trade in zip(entry_hours, closed_trades):
+        if hour not in profits_by_hour:
+            profits_by_hour[hour] = 0
+        profits_by_hour[hour] += trade.profit
+
+    # Sorting by hour for plotting
+    sorted_hours = sorted(profits_by_hour.keys())
+    sorted_profits = [profits_by_hour[hour] for hour in sorted_hours]
+
+    sns.barplot(x=sorted_hours, y=sorted_profits, ax=axs[2, 1])
+    axs[2, 1].set_title('Profits by Hour of Day (Entry)')
+    axs[2, 1].set_xlabel('Hour of Day')
+    axs[2, 1].set_ylabel('Profit')
+
+    # Plot 9: Profits Based on Hour of the Day - based on Exit
+    exit_hours = [trade.exit_time.hour for trade in closed_trades if trade.exit_time is not None]
+    profits_by_hour = {}
+    for hour, trade in zip(exit_hours, closed_trades):
+        if hour not in profits_by_hour:
+            profits_by_hour[hour] = 0
+        profits_by_hour[hour] += trade.profit
+
+    # Sorting by hour for plotting
+    sorted_hours = sorted(profits_by_hour.keys())
+    sorted_profits = [profits_by_hour[hour] for hour in sorted_hours]
+
+    sns.barplot(x=sorted_hours, y=sorted_profits, ax=axs[2, 2])
+    axs[2, 2].set_title('Profits by Hour of Day (Exit)')
+    axs[2, 2].set_xlabel('Hour of Day')
+    axs[2, 2].set_ylabel('Profit')
+
+    # Calculate profits by day of the week
+    day_of_week_profits = {i: 0 for i in range(7)}  # Dictionary to store profits for each day of the week
+
+    for trade in trades:
+        if trade.status == TradeStatus.CLOSED:
+            day_of_week = trade.exit_time.weekday()  # Monday is 0 and Sunday is 6
+            day_of_week_profits[day_of_week] += trade.profit
+
+    days = ['Mo', 'Tue', 'Wed', 'Thu', 'Fri']
+    # Additional Plot: Strategy Performance by Day of the Week
+    axs[2, 3].bar(days, [day_of_week_profits[i] for i in range(5)])
+    axs[2, 3].set_title('Profit by Day of the Week')
+    axs[2, 3].set_xlabel('Day of the Week')
+    axs[2, 3].set_ylabel('Cumulative Profit')    
+
+    #filename 
+    file = batch_id if batch_id is not None else runner_ids[0]
+    image_file_name = f"{file}.png"
+    image_path = str(MEDIA_DIRECTORY / "basic" / image_file_name)
+
+    # Adjust layout and save the combined plot as an image
+    plt.tight_layout()
+
+    if stream is False:
+        plt.savefig(image_path)
+        plt.close()
+    else:
+        # Return the image as a BytesIO stream
+        img_stream = BytesIO()
+        plt.savefig(img_stream, format='png')
+        plt.close()
+        img_stream.seek(0)  # Rewind the stream to the beginning
+        return 0, img_stream        
+
+# Example usage
+# trades = [list of Trade objects]
+if __name__ == '__main__':
+    id_list = ["c3e31cb5-ddf9-467e-a932-2118f6844355"]
+    generate_trading_report_image(runner_ids=id_list)
+    # batch_id = "90973e57"
+    # generate_trading_report_image(batch_id=batch_id)

v2realbot/static/index.html

@@ -279,7 +279,9 @@
                     </div>
                 </div>
             </div>
-
+            <div id="imagePreview" onclick="hideImage()">
+                <img src="" id="previewImg" style="width: auto; height: auto;" />
+            </div>
             <div id="archive-table" class="flex-items">
                 <label data-bs-toggle="collapse" data-bs-target="#archive-table-inner">
                     <h4>Past Runs</h4>
@@ -299,6 +301,7 @@
                         <button id="button_selpage" class="btn btn-outline-success btn-sm">Select all</button>
                         <button id="button_export_xml" class="btn btn-outline-success btn-sm">Export xml</button>
                         <button id="button_export_csv" class="btn btn-outline-success btn-sm">Export csv</button>
+                        <button id="button_report" class="btn btn-outline-success btn-sm">Report</button>
                         <!-- <button id="button_stopall" class="btn btn-outline-success btn-sm">Stop All</button>
                         <button id="button_refresh" class="btn btn-outline-success btn-sm">Refresh</button> -->
                     </div>

v2realbot/static/js/archivetables.js

@@ -110,9 +110,61 @@ function prepare_export() {
     return trdList
 }
 
+function display_image(imageUrl) {
+    // Attempt to load the image
+    var img = new Image();
+    img.src = imageUrl;
+    img.onload = function() {
+        // If the image loads successfully, display it
+        $('#previewImg').attr('src', imageUrl);
+        $('#imagePreview').show();
+    };
+    img.onerror = function() {
+        console.log("no image available")
+        // If the image fails to load, do nothing
+    };
+}
+
 $(document).ready(function () {
     archiveRecords.ajax.reload();
 
+    // Use 'td:nth-child(2)' to target the second column
+    $('#archiveTable tbody').on('click', 'td:nth-child(2)', function () {
+        var data = archiveRecords.row(this).data();
+        //var imageUrl = '/media/report_'+data.id+".png"; // Replace with your logic to get image URL
+        var imageUrl = '/media/basic/'+data.id+'.png'; // Replace with your logic to get image URL
+        console.log(imageUrl)
+        display_image(imageUrl)
+    });
+
+    // Use 'td:nth-child(2)' to target the second column
+    $('#archiveTable tbody').on('click', 'td:nth-child(18)', function () {
+        var data = archiveRecords.row(this).data();
+        if (data.batch_id) {
+            //var imageUrl = '/media/report_'+data.id+".png"; // Replace with your logic to get image URL
+            var imageUrl = '/media/basic/'+data.batch_id+'.png'; // Replace with your logic to get image URL
+            console.log(imageUrl)
+            display_image(imageUrl)
+        }
+    });    
+
+    // $('#archiveTable tbody').on('mouseleave', 'td:nth-child(2)', function () {
+    //     $('#imagePreview').hide();
+    // });
+
+    // Hide image on click anywhere in the document
+    $(document).on('click', function() {
+        $('#imagePreview').hide();
+    });
+
+    function hideImage() {
+        $('#imagePreview').hide();
+    }
+    // $('#archiveTable tbody').on('mousemove', 'td:nth-child(2)', function(e) {
+    //     $('#imagePreview').css({'top': e.pageY + 10, 'left': e.pageX + 10});
+    // });
+
+
     //button export
     $('#button_export_xml').click(function () {
             xmled = convertToXml(prepare_export())
@@ -360,6 +412,46 @@ $(document).ready(function () {
         }
     });
 
+    //generate report button
+    $('#button_report').click(function () {
+        rows = archiveRecords.rows('.selected');
+        if (rows == undefined) {
+            return
+        }
+        runnerIds = []
+        if(rows.data().length > 0 ) {
+            // Loop through the selected rows and display an alert with each row's ID
+            rows.every(function (rowIdx, tableLoop, rowLoop ) {
+                var data = this.data()
+                runnerIds.push(data.id);
+            });
+        }
+        $.ajax({
+            url:"/archived_runners/generatereportimage",
+            beforeSend: function (xhr) {
+                xhr.setRequestHeader('X-API-Key',
+                    API_KEY); },
+            method:"POST",
+            xhrFields: {
+                responseType: 'blob'
+            },
+            contentType: "application/json",
+            processData: false,
+            data: JSON.stringify(runnerIds),
+            success:function(blob){		
+                var url = window.URL || window.webkitURL;
+                console.log("vraceny obraz", blob)
+                console.log("url",url.createObjectURL(blob))
+                display_image(url.createObjectURL(blob))
+            },
+            error: function(xhr, status, error) {
+                console.log("proc to skace do erroru?")
+                //window.alert(JSON.stringify(xhr));
+                console.log(JSON.stringify(xhr));             
+            }
+        })
+    });
+
 
     //delete button
     $('#button_delete_arch').click(function () {

v2realbot/static/main.css

@@ -164,7 +164,14 @@ table.dataTable thead>tr>th.sorting_asc:before, table.dataTable thead>tr>th.sort
   --bs-gradient: none;
 }
 
-
+#imagePreview {
+  display: none; 
+  position: fixed; 
+  z-index: 100;
+  left: 50%;
+  top: 50%;
+  transform: translate(-50%, -50%);
+}
 
 /* .btn-outline-success {
   --bs-btn-color: #316164;

v2realbot/strategy/StrategyClassicSL.py

@@ -129,14 +129,27 @@ class StrategyClassicSL(Strategy):
 
                 #pokud jde o finalni FILL - pridame do pole tento celkovy relativnich profit (ze ktereho se pocita kumulativni relativni profit)
                 rel_profit_cum_calculated = 0
-
+                partial_exit = False
+                partial_last = False
                 if data.event == TradeEvent.FILL:
-                    #TODO pokud mame partial exit, tak se spravne vypocita relativni profit, ale
-                    #  je jen na mensi mnozszvi take z nej delat cum_calculate je blbost - OPRAVIT
-                    self.state.rel_profit_cum.append(rel_profit)
-                    rel_profit_cum_calculated = round(np.sum(self.state.rel_profit_cum),5)
+                    #jde o partial EXIT dvááme si rel.profit do docasne promenne, po poslednim exitu z nich vypocteme skutecny rel.profit
+                    if data.position_qty != 0:
+                        self.state.docasny_rel_profit.append(rel_profit)
+                        partial_exit = True
+                    else:
+                        #jde o posledni z PARTIAL EXITU tzn.data.position_qty == 0
+                        if len(self.state.docasny_rel_profit) > 0:
+                            #pricteme aktualni rel profit
+                            self.state.docasny_rel_profit.append(rel_profit)
+                            #a z rel profitu tohoto tradu vypocteme prumer, ktery teprve ulozime
+                            rel_profit = round(np.mean(self.state.docasny_rel_profit),5)
+                            self.state.docasny_rel_profit = []
+                            partial_last = True
 
-                self.state.ilog(e=f"BUY notif - SHORT PROFIT:{round(float(trade_profit),3)} celkem:{round(float(self.state.profit),3)} rel:{float(rel_profit)} rel_cum:{round(rel_profit_cum_calculated,7)}", msg=str(data.event), rel_profit_cum=str(self.state.rel_profit_cum), bought_amount=bought_amount, avg_costs=avg_costs, trade_qty=data.qty, trade_price=data.price, orderid=str(data.order.id))
+                        self.state.rel_profit_cum.append(rel_profit)
+                        rel_profit_cum_calculated = round(np.sum(self.state.rel_profit_cum),5)
+
+                self.state.ilog(e=f"BUY notif - SHORT PROFIT: {partial_exit=} {partial_last=} {round(float(trade_profit),3)} celkem:{round(float(self.state.profit),3)} rel:{float(rel_profit)} rel_cum:{round(rel_profit_cum_calculated,7)}", msg=str(data.event), rel_profit_cum=str(self.state.rel_profit_cum), bought_amount=bought_amount, avg_costs=avg_costs, trade_qty=data.qty, trade_price=data.price, orderid=str(data.order.id))
 
                 #zapsat profit do prescr.trades
                 for trade in self.state.vars.prescribedTrades:
@@ -260,12 +273,27 @@ class StrategyClassicSL(Strategy):
                     rel_profit = round((trade_profit / (vstup_cena * float(data.order.qty))) * 100,5)
 
                 rel_profit_cum_calculated = 0
-                #pokud jde o finalni FILL - pridame do pole relativnich profit (ze ktereho se pocita kumulativni relativni profit)
+                partial_exit = False
+                partial_last = False
                 if data.event == TradeEvent.FILL:
-                    self.state.rel_profit_cum.append(rel_profit)
-                    rel_profit_cum_calculated = round(np.sum(self.state.rel_profit_cum),5)
+                    #jde o partial EXIT dvááme si rel.profit do docasne promenne, po poslednim exitu z nich vypocteme skutecny rel.profit
+                    if data.position_qty != 0:
+                        self.state.docasny_rel_profit.append(rel_profit)
+                        partial_exit = True
+                    else:
+                        #jde o posledni z PARTIAL EXITU tzn.data.position_qty == 0
+                        if len(self.state.docasny_rel_profit) > 0:
+                            #pricteme aktualni rel profit
+                            self.state.docasny_rel_profit.append(rel_profit)
+                            #a z rel profitu tohoto tradu vypocteme prumer, ktery teprve ulozime
+                            rel_profit = round(np.mean(self.state.docasny_rel_profit),5)
+                            self.state.docasny_rel_profit = []
+                            partial_last = True
 
-                self.state.ilog(e=f"SELL notif - PROFIT:{round(float(trade_profit),3)} celkem:{round(float(self.state.profit),3)} rel:{float(rel_profit)} rel_cum:{round(rel_profit_cum_calculated,7)}", msg=str(data.event), rel_profit_cum = str(self.state.rel_profit_cum), sold_amount=sold_amount, avg_costs=avg_costs, trade_qty=data.qty, trade_price=data.price, orderid=str(data.order.id))
+                        self.state.rel_profit_cum.append(rel_profit)
+                        rel_profit_cum_calculated = round(np.sum(self.state.rel_profit_cum),5)
+
+                self.state.ilog(e=f"SELL notif - LONG PROFIT {partial_exit=} {partial_last=}:{round(float(trade_profit),3)} celkem:{round(float(self.state.profit),3)} rel:{float(rel_profit)} rel_cum:{round(rel_profit_cum_calculated,7)}", msg=str(data.event), rel_profit_cum = str(self.state.rel_profit_cum), sold_amount=sold_amount, avg_costs=avg_costs, trade_qty=data.qty, trade_price=data.price, orderid=str(data.order.id))
 
                 #zapsat profit do prescr.trades
                 for trade in self.state.vars.prescribedTrades:

v2realbot/strategy/base.py

@@ -707,6 +707,7 @@ class StrategyState:
         self.runner_id = runner_id
         self.bt = bt
         self.dont_exit_already_activated = False
+        self.docasny_rel_profit = []
         self.ilog_save = ilog_save
         self.sl_optimizer_short = optimsl.SLOptimizer(ptm.TradeDirection.SHORT)
         self.sl_optimizer_long = optimsl.SLOptimizer(ptm.TradeDirection.LONG)

v2realbot/strategyblocks/indicators/helpers.py

@@ -82,7 +82,10 @@ def get_source_series(state, source: str):
         try:
             return state.bars[source]
         except KeyError:
-            return state.indicators[source]
+            try:
+                return state.indicators[source]
+            except KeyError:
+                return None
     else:
         dict_name = source[:split_index]
         key = source[split_index + 1:]

v2realbot/strategyblocks/newtrade/signals.py

@@ -7,6 +7,7 @@ from datetime import datetime
 from rich import print as printanyway
 from traceback import format_exc
 from v2realbot.strategyblocks.newtrade.conditions import go_conditions_met, common_go_preconditions_check
+from v2realbot.strategyblocks.newtrade.sizing import get_size, get_multiplier
 
 def signal_search(state: StrategyState, data):
     # SIGNAL sekce ve stratvars obsahuje signaly: Ty se skladaji z obecnych parametru a podsekce podminek.
@@ -42,7 +43,7 @@ def execute_signal_generator(state, data, name):
     options = safe_get(state.vars.signals, name, None)
 
     if options is None:
-        state.ilog(lvl=1,e="No options for {name} in stratvars")
+        state.ilog(lvl=1,e=f"No options for {name} in stratvars")
         return
     
     if common_go_preconditions_check(state, data, signalname=name, options=options) is False:
@@ -71,20 +72,26 @@ def execute_signal_generator(state, data, name):
         if long_enabled is False:
             state.ilog(lvl=1,e=f"{name} LONG DISABLED")
         if long_enabled and go_conditions_met(state, data,signalname=name, direction=TradeDirection.LONG):
+            multiplier = get_multiplier(state, data, options, TradeDirection.LONG)
             state.vars.prescribedTrades.append(Trade(
                                     id=uuid4(),
                                     last_update=datetime.fromtimestamp(state.time).astimezone(zoneNY),
                                     status=TradeStatus.READY,
                                     generated_by=name,
+                                    size=multiplier*state.vars.chunk,
+                                    size_multiplier = multiplier,
                                     direction=TradeDirection.LONG,
                                     entry_price=None,
                                     stoploss_value = None))
         elif short_enabled and go_conditions_met(state, data, signalname=name, direction=TradeDirection.SHORT):
+            multiplier = get_multiplier(state, data, options, TradeDirection.SHORT)
             state.vars.prescribedTrades.append(Trade(
                     id=uuid4(),
                     last_update=datetime.fromtimestamp(state.time).astimezone(zoneNY),
                     status=TradeStatus.READY,
                     generated_by=name,
+                    size=multiplier*state.vars.chunk,
+                    size_multiplier = multiplier,
                     direction=TradeDirection.SHORT,
                     entry_price=None,
                     stoploss_value = None))

v2realbot/strategyblocks/newtrade/sizing.py

@@ -0,0 +1,143 @@
+from v2realbot.strategy.base import StrategyState
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
+import v2realbot.utils.utils as utls
+from v2realbot.config import KW
+from uuid import uuid4
+from datetime import datetime
+from rich import print as printanyway
+from traceback import format_exc
+from v2realbot.strategyblocks.newtrade.conditions import go_conditions_met, common_go_preconditions_check
+from v2realbot.strategyblocks.indicators.helpers import get_source_series
+import numpy as np
+
+def get_size(state: StrategyState, data, signaloptions: dict, direction: TradeDirection):
+    return state.vars.chunk * get_multiplier(state, signaloptions, direction)
+
+def get_multiplier(state: StrategyState, data, signaloptions: dict, direction: TradeDirection):
+    """"
+    Function return dynamic sizing multiplier according to directive and current trades.
+
+    Default: state.vars.chunk
+
+    Additional sizing logic is layered on top of each other according to directives.
+
+    Currently supporting:
+    1) pattern sizing U-shape, hat-shape (x - bars, minutes, y - sizing multiplier 0 to 1 )
+    2) probes
+
+    Future ideas:
+    - ML sizing model
+
+    DIRECTIVES:
+    #sondy na zacatku market
+    probe_enabled = true # sonda zapnuta
+    number = 1 # pocet sond
+    probe_size = 0.01 #velikost sondy, nasobek def size
+
+    #pattern - dynamicka uprava na zaklade casu
+    pattern_enabled = true
+    pattern_source = "minutes" #or any series - indicators, bars or state etc. (index[-1], np.sum(state.rel_profit_cum)...)
+    pattern_source_vals = [0,30,90, 200, 300, 390]
+    pattern_sizing_vals = [0.1,0.5, 0.8, 1, 0.6, 0.1]
+    
+    #np.interp(atr10, [0.001, 0.06], [1, 5])
+    #size_multiplier = np.interp(pattern_source, [SIZING_pattern_source_vals], [SIZING_pattern_sizing_vals])
+    
+
+    #TODO 
+    - pomocna graf pro vizualizaci interpolace - v tools/sizingpatternvisual.py
+    - dopsat do dokumentace direktiv - do tabulky
+    - ukládat sizing coeff do prescrTrades
+    - upravit výpočet denního relativniho profitu u tradu na základě vstupního sizing koeficientu
+    - vyresit zda max_oss_to_quit_rel aplikovat bud per alokovana pozice nebo trade 
+    (pokud mam na trade, pak mi zafunguje i na minimalni sodnu) Zatim bude
+    realizován takto
+
+        - nejprve ověří rel profit tradu a pokud přesáhne, strategie se suspendne
+        - poté se rel profit tradu vynásobí multiplikátorem a započte se do denního rel profitu, jehož
+          výše se následně také ověří
+
+    NOTE: zatim neupraveno, a do denniho rel profitu se zapocitava plnym pomerem, diky tomu
+    si muzu dat i na sondu -0.5 suspend strategie. Nevyhoda: rel.profit presne neodpovida
+
+    [stratvars.signals.morning1.sizing] #specificke pro dany signal
+    probe_enabled = true
+    probe_size = 0.01
+    pattern_enabled = true
+    # pattern_source = "minutes" #or any series - indicators, bars or state etc. (index[-1], np.sum(state.rel_profit_cum)...)
+    pattern_source_axis = [0,30,90, 200, 300, 390]
+    pattern_size_axis = [0.1,0.5, 0.8, 1, 0.6, 0.1]   
+
+    [stratvars.sizing] #obecne jako fallback pro vsechny signaly
+    probe_enabled = true
+    probe_size = 0.01
+    pattern_enabled = true
+    # pattern_source = "minutes" #or any series - indicators, bars or state etc. (index[-1], np.sum(state.rel_profit_cum)...)
+    pattern_source_axis = [0,30,90, 200, 300, 390]
+    pattern_size_axis = [0.1,0.5, 0.8, 1, 0.6, 0.1]
+
+    """""
+    multiplier = 1
+
+    #fallback common sizing sekci
+    fallback_options = utls.safe_get(state.vars, 'sizing', None)
+
+    #signal specific sekce
+    options = utls.safe_get(signaloptions, 'sizing', fallback_options)
+
+    if options is None:
+        state.ilog(lvl=1,e="No sizing options common or signal specific in stratvars")
+        return multiplier
+
+    #PROBE ENABLED
+    # probe_enabled = true # sonda zapnuta
+    # probe_number = 1 # pocet sond
+    # probe_size = 0.01 #velikost sondy, nasobek def size
+
+    probe_enabled = utls.safe_get(options, "probe_enabled", False)
+
+    if probe_enabled:
+        #zatim pouze probe number 1 natvrdo, tzn. nesmi byt trade pro aktivace
+        if state.vars.last_in_index is None:
+            #probe_number = utls.safe_get(options, "probe_number",1)
+            probe_size = float(utls.safe_get(options, "probe_size", 0.1))
+            state.ilog(lvl=1,e=f"SIZER - PROBE - setting multiplier to {probe_size}", options=options)
+            return probe_size
+
+    #SIZING PATTER
+    # pattern_enabled = true
+    # pattern_source = "minutes" #or any series - indicators, bars or state etc. (index[-1], np.sum(state.rel_profit_cum)...)
+    # pattern_source_axis = [0,30,90, 200, 300, 390]
+    # pattern_size_axis = [0.1,0.5, 0.8, 1, 0.6, 0.1]  
+    pattern_enabled = utls.safe_get(options, "pattern_enabled", False)
+
+    if pattern_enabled:
+        input_value = None
+        pattern_source = utls.safe_get(options, "pattern_source", "minutes")
+
+        #TODO do budoucna mozna sem dát libovolnou series např. index, time, profit, rel_profit?
+        if pattern_source != "minutes":
+
+            input_value = eval(pattern_source, {'state': state, 'np': np, 'utls': utls}, state.ind_mapping)
+
+            if input_value is None:
+                state.ilog(lvl=1,e=f"SIZER - ERROR Pattern source is None, after evaluation of expression", options=str(options))
+                return multiplier
+        else:
+            input_value = utls.minutes_since_market_open(datetime.fromtimestamp(data['updated']).astimezone(utls.zoneNY))
+
+        pattern_source_axis = utls.safe_get(options, "pattern_source_axis", None)
+        pattern_size_axis = utls.safe_get(options, "pattern_size_axis", None)
+
+        if pattern_source_axis is None or pattern_size_axis is None:
+            state.ilog(lvl=1,e=f"SIZER - Pattern source  and size axis must be set", options=str(options))
+            return multiplier
+
+        state.ilog(lvl=1,e=f"SIZER - Input value of {pattern_source} value {input_value}", options=options, time=state.time)
+        multiplier = np.interp(input_value, pattern_source_axis, pattern_size_axis)
+        state.ilog(lvl=1,e=f"SIZER - Interpolated value  {multiplier}", input_value=input_value, pattern_source_axis=pattern_source_axis, pattern_size_axis=pattern_size_axis, options=options, time=state.time)
+    
+    if multiplier > 1 or multiplier <= 0:
+        state.ilog(lvl=1,e=f"SIZER - Mame nekde problem MULTIPLIER mimo RANGE ERROR {multiplier}", options=options, time=state.time)
+        multiplier = 1
+    return multiplier

v2realbot/tools/sizingpattervisual.py

@@ -0,0 +1,29 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Sem zadat pattern X a Y pro VIZUALIZACI
+
+#minutes
+pattern_x = [0, 30, 90, 200, 300, 390]
+pattern_y = [0.1, 0.5, 0.8, 1, 0.6, 0.1]
+
+#bar index - použitelné u time scale barů
+pattern_x = [0, 30, 90, 200, 300, 390]
+pattern_y = [0.1, 0.5, 0.8, 1, 0.6, 0.1]
+
+#celkový profit
+
+# Generating a range of input values for interpolation
+input_values = np.linspace(min(pattern_x), max(pattern_x), 500)
+multipliers = np.interp(input_values, pattern_x, pattern_y)
+
+# Plotting
+plt.figure(figsize=(10, 6))
+plt.plot(pattern_x, pattern_y, 'o', label='Original Points')
+plt.plot(input_values, multipliers, label='Interpolated Values')
+plt.xlabel('X values')
+plt.ylabel('Interpolated Multipliers')
+plt.title('Interpolation Chart')
+plt.legend()
+plt.grid(True)
+plt.show()

v2realbot/utils/utils.py

@@ -464,6 +464,30 @@ def is_open_rush(dt: datetime, mins: int = 30):
     rushtime = (datetime.combine(date.today(), business_hours["from"]) + timedelta(minutes=mins)).time()
     return business_hours["from"] <= dt.time() < rushtime
 
+
+#TODO v budoucnu predelat - v initu nacist jednou market open cas a ten pouzivat vsude
+#kde je treba (ted je tady natvrdo 9.30)
+def minutes_since_market_open(datetime_aware: datetime):
+    """
+    Calculate the number of minutes elapsed from 9:30 AM to the given timezone-aware datetime of the same day.
+    This version is optimized for speed and should be used when calling in a loop.
+
+    :param datetime_aware: A timezone-aware datetime object representing the time to compare.
+    :return: The number of minutes since today's 9:30 AM.
+    """
+    # Ensure the input datetime is timezone-aware
+    if datetime_aware.tzinfo is None or datetime_aware.tzinfo.utcoffset(datetime_aware) is None:
+        raise ValueError("The input datetime must be timezone-aware.")
+
+    # Calculate minutes since midnight for both times
+    minutes_since_midnight = datetime_aware.hour * 60 + datetime_aware.minute
+    morning_minutes = 9 * 60 + 30
+
+    # Calculate the difference
+    delta_minutes = minutes_since_midnight - morning_minutes
+
+    return delta_minutes if delta_minutes >= 0 else 0
+
 #optimalized by BARD
 def is_window_open(dt: datetime, start: int = 0, end: int = 390):
     """"