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My home-made bar replay for MT4

I made a home-made bar replay for MT4 as an alternative to the tradingview bar replay. You can change timeframes and use objects easily. It just uses vertical lines to block the future candles. Then it adjusts the vertical lines when you change zoom or time frames to keep the "future" bars hidden.
I am not a professional coder so this is not as robust as something like Soft4fx or Forex Tester. But for me it gets the job done and is very convenient. Maybe you will find some benefit from it.

Here are the steps to use it:
1) copy the text from the code block
2) go to MT4 terminal and open Meta Editor (click icon or press F4)
3) go to File -> New -> Expert Advisor
4) put in a title and click Next, Next, Finish
5) Delete all text from new file and paste in text from code block
6) go back to MT4
7) Bring up Navigator (Ctrl+N if it's not already up)
8) go to expert advisors section and find what you titled it
9) open up a chart of the symbol you want to test
10) add the EA to this chart
11) specify colors and start time in inputs then press OK
12) use "S" key on your keyboard to advance 1 bar of current time frame
13) use tool bar buttons to change zoom and time frames, do objects, etc.
14) don't turn on auto scroll. if you do by accident, press "S" to return to simulation time.
15) click "buy" and "sell" buttons (white text, top center) to generate entry, TP and SL lines to track your trade
16) to cancel or close a trade, press "close order" then click the white entry line
17) drag and drop TP/SL lines to modify RR
18) click "End" to delete all objects and remove simulation from chart
19) to change simulation time, click "End", then add the simulator EA to your chart with a new start time
20) When you click "End", your own objects will be deleted too, so make sure you are done with them
21) keep track of your own trade results manually
22) use Tools-> History center to download new data if you need it. the simulator won't work on time frames if you don't have historical data going back that far, but it will work on time frames that you have the data for. If you have data but its not appearing, you might also need to increase max bars in chart in Tools->Options->Charts.
23) don't look at status bar if you are moused over hidden candles, or to avoid this you can hide the status bar.


Here is the code block.
//+------------------------------------------------------------------+ //| Bar Replay V2.mq4 | //| Copyright 2020, MetaQuotes Software Corp. | //| https://www.mql5.com | //+------------------------------------------------------------------+ #property copyright "Copyright 2020, MetaQuotes Software Corp." #property link "https://www.mql5.com" #property version "1.00" #property strict #define VK_A 0x41 #define VK_S 0x53 #define VK_X 0x58 #define VK_Z 0x5A #define VK_V 0x56 #define VK_C 0x43 #define VK_W 0x57 #define VK_E 0x45 double balance; string balance_as_string; int filehandle; int trade_ticket = 1; string objectname; string entry_line_name; string tp_line_name; string sl_line_name; string one_R_line_name; double distance; double entry_price; double tp_price; double sl_price; double one_R; double TP_distance; double gain_in_R; string direction; bool balance_file_exist; double new_balance; double sl_distance; string trade_number; double risk; double reward; string RR_string; int is_tp_or_sl_line=0; int click_to_cancel=0; input color foreground_color = clrWhite; input color background_color = clrBlack; input color bear_candle_color = clrRed; input color bull_candle_color = clrSpringGreen; input color current_price_line_color = clrGray; input string start_time = "2020.10.27 12:00"; input int vertical_margin = 100; //+------------------------------------------------------------------+ //| Expert initialization function | //+------------------------------------------------------------------+ int OnInit() { Comment(""); ChartNavigate(0,CHART_BEGIN,0); BlankChart(); ChartSetInteger(0,CHART_SHIFT,true); ChartSetInteger(0,CHART_FOREGROUND,false); ChartSetInteger(0,CHART_AUTOSCROLL,false); ChartSetInteger(0,CHART_SCALEFIX,false); ChartSetInteger(0,CHART_SHOW_OBJECT_DESCR,true); if (ObjectFind(0,"First OnInit")<0){ CreateStorageHLine("First OnInit",1);} if (ObjectFind(0,"Simulation Time")<0){ CreateTestVLine("Simulation Time",StringToTime(start_time));} string vlinename; for (int i=0; i<=1000000; i++){ vlinename="VLine"+IntegerToString(i); ObjectDelete(vlinename); } HideBars(SimulationBarTime(),0); //HideBar(SimulationBarTime()); UnBlankChart(); LabelCreate("New Buy Button","Buy",0,38,foreground_color); LabelCreate("New Sell Button","Sell",0,41,foreground_color); LabelCreate("Cancel Order","Close Order",0,44,foreground_color); LabelCreate("Risk To Reward","RR",0,52,foreground_color); LabelCreate("End","End",0,35,foreground_color); ObjectMove(0,"First OnInit",0,0,0); //--- create timer EventSetTimer(60); return(INIT_SUCCEEDED); } //+------------------------------------------------------------------+ //| Expert deinitialization function | //+------------------------------------------------------------------+ void OnDeinit(const int reason) { //--- destroy timer EventKillTimer(); } //+------------------------------------------------------------------+ //| Expert tick function | //+------------------------------------------------------------------+ void OnTick() { //--- } //+------------------------------------------------------------------+ //| ChartEvent function | //+------------------------------------------------------------------+ void OnChartEvent(const int id, const long &lparam, const double &dparam, const string &sparam) { if (id==CHARTEVENT_CHART_CHANGE){ int chartscale = ChartGetInteger(0,CHART_SCALE,0); int lastchartscale = ObjectGetDouble(0,"Last Chart Scale",OBJPROP_PRICE,0); if (chartscale!=lastchartscale){ int chartscale = ChartGetInteger(0,CHART_SCALE,0); ObjectMove(0,"Last Chart Scale",0,0,chartscale); OnInit(); }} if (id==CHARTEVENT_KEYDOWN){ if (lparam==VK_S){ IncreaseSimulationTime(); UnHideBar(SimulationPosition()); NavigateToSimulationPosition(); CreateHLine(0,"Current Price",Close[SimulationPosition()+1],current_price_line_color,1,0,true,false,false,"price"); SetChartMinMax(); }} if(id==CHARTEVENT_OBJECT_CLICK) { if(sparam=="New Sell Button") { distance = iATR(_Symbol,_Period,20,SimulationPosition()+1)/2; objectname = "Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1],foreground_color,2,5,false,true,true,"Sell"); objectname = "TP for Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1]-distance*2,clrAqua,2,5,false,true,true,"TP"); objectname = "SL for Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1]+distance,clrRed,2,5,false,true,true,"SL"); trade_ticket+=1; } } if(id==CHARTEVENT_OBJECT_CLICK) { if(sparam=="New Buy Button") { distance = iATR(_Symbol,_Period,20,SimulationPosition()+1)/2; objectname = "Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1],foreground_color,2,5,false,true,true,"Buy"); objectname = "TP for Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1]+distance*2,clrAqua,2,5,false,true,true,"TP"); objectname = "SL for Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1]-distance,clrRed,2,5,false,true,true,"SL"); trade_ticket+=1; } } if(id==CHARTEVENT_OBJECT_DRAG) { if(StringFind(sparam,"TP",0)==0) { is_tp_or_sl_line=1; } if(StringFind(sparam,"SL",0)==0) { is_tp_or_sl_line=1; } Comment(is_tp_or_sl_line); if(is_tp_or_sl_line==1) { trade_number = StringSubstr(sparam,7,9); entry_line_name = trade_number; tp_line_name = "TP for "+entry_line_name; sl_line_name = "SL for "+entry_line_name; entry_price = ObjectGetDouble(0,entry_line_name,OBJPROP_PRICE,0); tp_price = ObjectGetDouble(0,tp_line_name,OBJPROP_PRICE,0); sl_price = ObjectGetDouble(0,sl_line_name,OBJPROP_PRICE,0); sl_distance = MathAbs(entry_price-sl_price); TP_distance = MathAbs(entry_price-tp_price); reward = TP_distance/sl_distance; RR_string = "RR = 1 : "+DoubleToString(reward,2); ObjectSetString(0,"Risk To Reward",OBJPROP_TEXT,RR_string); is_tp_or_sl_line=0; } } if(id==CHARTEVENT_OBJECT_CLICK) { if(sparam=="Cancel Order") { click_to_cancel=1; Comment("please click the entry line of the order you wish to cancel."); } } if(id==CHARTEVENT_OBJECT_CLICK) { if(sparam!="Cancel Order") { if(click_to_cancel==1) { if(ObjectGetInteger(0,sparam,OBJPROP_TYPE,0)==OBJ_HLINE) { entry_line_name = sparam; tp_line_name = "TP for "+sparam; sl_line_name = "SL for "+sparam; ObjectDelete(0,entry_line_name); ObjectDelete(0,tp_line_name); ObjectDelete(0,sl_line_name); click_to_cancel=0; ObjectSetString(0,"Risk To Reward",OBJPROP_TEXT,"RR"); } } } } if (id==CHARTEVENT_OBJECT_CLICK){ if (sparam=="End"){ ObjectsDeleteAll(0,-1,-1); ExpertRemove(); }} } //+------------------------------------------------------------------+ void CreateStorageHLine(string name, double value){ ObjectDelete(name); ObjectCreate(0,name,OBJ_HLINE,0,0,value); ObjectSetInteger(0,name,OBJPROP_SELECTED,false); ObjectSetInteger(0,name,OBJPROP_SELECTABLE,false); ObjectSetInteger(0,name,OBJPROP_COLOR,clrNONE); ObjectSetInteger(0,name,OBJPROP_BACK,true); ObjectSetInteger(0,name,OBJPROP_ZORDER,0); } void CreateTestHLine(string name, double value){ ObjectDelete(name); ObjectCreate(0,name,OBJ_HLINE,0,0,value); ObjectSetInteger(0,name,OBJPROP_SELECTED,false); ObjectSetInteger(0,name,OBJPROP_SELECTABLE,false); ObjectSetInteger(0,name,OBJPROP_COLOR,clrWhite); ObjectSetInteger(0,name,OBJPROP_BACK,true); ObjectSetInteger(0,name,OBJPROP_ZORDER,0); } bool IsFirstOnInit(){ bool bbb=false; if (ObjectGetDouble(0,"First OnInit",OBJPROP_PRICE,0)==1){return true;} return bbb; } void CreateTestVLine(string name, datetime timevalue){ ObjectDelete(name); ObjectCreate(0,name,OBJ_VLINE,0,timevalue,0); ObjectSetInteger(0,name,OBJPROP_SELECTED,false); ObjectSetInteger(0,name,OBJPROP_SELECTABLE,false); ObjectSetInteger(0,name,OBJPROP_COLOR,clrNONE); ObjectSetInteger(0,name,OBJPROP_BACK,false); ObjectSetInteger(0,name,OBJPROP_ZORDER,3); } datetime SimulationTime(){ return ObjectGetInteger(0,"Simulation Time",OBJPROP_TIME,0); } int SimulationPosition(){ return iBarShift(_Symbol,_Period,SimulationTime(),false); } datetime SimulationBarTime(){ return Time[SimulationPosition()]; } void IncreaseSimulationTime(){ ObjectMove(0,"Simulation Time",0,Time[SimulationPosition()-1],0); } void NavigateToSimulationPosition(){ ChartNavigate(0,CHART_END,-1*SimulationPosition()+15); } void NotifyNotEnoughHistoricalData(){ BlankChart(); Comment("Sorry, but there is not enough historical data to load this time frame."+"\n"+ "Please load more historical data or use a higher time frame. Thank you :)");} void UnHideBar(int barindex){ ObjectDelete(0,"VLine"+IntegerToString(barindex+1)); } void BlankChart(){ ChartSetInteger(0,CHART_COLOR_FOREGROUND,clrNONE); ChartSetInteger(0,CHART_COLOR_CANDLE_BEAR,clrNONE); ChartSetInteger(0,CHART_COLOR_CANDLE_BULL,clrNONE); ChartSetInteger(0,CHART_COLOR_CHART_DOWN,clrNONE); ChartSetInteger(0,CHART_COLOR_CHART_UP,clrNONE); ChartSetInteger(0,CHART_COLOR_CHART_LINE,clrNONE); ChartSetInteger(0,CHART_COLOR_GRID,clrNONE); ChartSetInteger(0,CHART_COLOR_ASK,clrNONE); ChartSetInteger(0,CHART_COLOR_BID,clrNONE);} void UnBlankChart(){ ChartSetInteger(0,CHART_COLOR_FOREGROUND,foreground_color); ChartSetInteger(0,CHART_COLOR_CANDLE_BEAR,bear_candle_color); ChartSetInteger(0,CHART_COLOR_CANDLE_BULL,bull_candle_color); ChartSetInteger(0,CHART_COLOR_BACKGROUND,background_color); ChartSetInteger(0,CHART_COLOR_CHART_DOWN,foreground_color); ChartSetInteger(0,CHART_COLOR_CHART_UP,foreground_color); ChartSetInteger(0,CHART_COLOR_CHART_LINE,foreground_color); ChartSetInteger(0,CHART_COLOR_GRID,clrNONE); ChartSetInteger(0,CHART_COLOR_ASK,clrNONE); ChartSetInteger(0,CHART_COLOR_BID,clrNONE);} void HideBars(datetime starttime, int shift){ int startbarindex = iBarShift(_Symbol,_Period,starttime,false); ChartNavigate(0,CHART_BEGIN,0); if (Time[WindowFirstVisibleBar()]>SimulationTime()){NotifyNotEnoughHistoricalData();} if (Time[WindowFirstVisibleBar()]=0; i--){ vlinename="VLine"+IntegerToString(i); ObjectCreate(0,vlinename,OBJ_VLINE,0,Time[i],0); ObjectSetInteger(0,vlinename,OBJPROP_COLOR,background_color); ObjectSetInteger(0,vlinename,OBJPROP_BACK,false); ObjectSetInteger(0,vlinename,OBJPROP_WIDTH,vlinewidth); ObjectSetInteger(0,vlinename,OBJPROP_ZORDER,10); ObjectSetInteger(0,vlinename,OBJPROP_FILL,true); ObjectSetInteger(0,vlinename,OBJPROP_STYLE,STYLE_SOLID); ObjectSetInteger(0,vlinename,OBJPROP_SELECTED,false); ObjectSetInteger(0,vlinename,OBJPROP_SELECTABLE,false); } NavigateToSimulationPosition(); SetChartMinMax();} }//end of HideBars function void SetChartMinMax(){ int firstbar = WindowFirstVisibleBar(); int lastbar = SimulationPosition(); int lastbarwhenscrolled = WindowFirstVisibleBar()-WindowBarsPerChart(); if (lastbarwhenscrolled>lastbar){lastbar=lastbarwhenscrolled;} double highest = High[iHighest(_Symbol,_Period,MODE_HIGH,firstbar-lastbar,lastbar)]; double lowest = Low[iLowest(_Symbol,_Period,MODE_LOW,firstbar-lastbar,lastbar)]; ChartSetInteger(0,CHART_SCALEFIX,true); ChartSetDouble(0,CHART_FIXED_MAX,highest+vertical_margin*_Point); ChartSetDouble(0,CHART_FIXED_MIN,lowest-vertical_margin*_Point); } void LabelCreate(string labelname, string labeltext, int row, int column, color labelcolor){ int ylocation = row*18; int xlocation = column*10; ObjectCreate(0,labelname,OBJ_LABEL,0,0,0); ObjectSetString(0,labelname,OBJPROP_TEXT,labeltext); ObjectSetInteger(0,labelname,OBJPROP_COLOR,labelcolor); ObjectSetInteger(0,labelname,OBJPROP_FONTSIZE,10); ObjectSetInteger(0,labelname,OBJPROP_ZORDER,10); ObjectSetInteger(0,labelname,OBJPROP_BACK,false); ObjectSetInteger(0,labelname,OBJPROP_CORNER,CORNER_LEFT_UPPER); ObjectSetInteger(0,labelname,OBJPROP_ANCHOR,ANCHOR_LEFT_UPPER); ObjectSetInteger(0,labelname,OBJPROP_XDISTANCE,xlocation); ObjectSetInteger(0,labelname,OBJPROP_YDISTANCE,ylocation);} double GetHLinePrice(string name){ return ObjectGetDouble(0,name,OBJPROP_PRICE,0); } void CreateHLine(int chartid, string objectnamey, double objectprice, color linecolor, int width, int zorder, bool back, bool selected, bool selectable, string descriptionn) { ObjectDelete(chartid,objectnamey); ObjectCreate(chartid,objectnamey,OBJ_HLINE,0,0,objectprice); ObjectSetString(chartid,objectnamey,OBJPROP_TEXT,objectprice); ObjectSetInteger(chartid,objectnamey,OBJPROP_COLOR,linecolor); ObjectSetInteger(chartid,objectnamey,OBJPROP_WIDTH,width); ObjectSetInteger(chartid,objectnamey,OBJPROP_ZORDER,zorder); ObjectSetInteger(chartid,objectnamey,OBJPROP_BACK,back); ObjectSetInteger(chartid,objectnamey,OBJPROP_SELECTED,selected); ObjectSetInteger(chartid,objectnamey,OBJPROP_SELECTABLE,selectable); ObjectSetString(0,objectnamey,OBJPROP_TEXT,descriptionn); } //end of code 
submitted by Learning_2 to Forex [link] [comments]

No, the British did not steal $45 trillion from India

This is an updated copy of the version on BadHistory. I plan to update it in accordance with the feedback I got.
I'd like to thank two people who will remain anonymous for helping me greatly with this post (you know who you are)
Three years ago a festschrift for Binay Bhushan Chaudhuri was published by Shubhra Chakrabarti, a history teacher at the University of Delhi and Utsa Patnaik, a Marxist economist who taught at JNU until 2010.
One of the essays in the festschirt by Utsa Patnaik was an attempt to quantify the "drain" undergone by India during British Rule. Her conclusion? Britain robbed India of $45 trillion (or £9.2 trillion) during their 200 or so years of rule. This figure was immensely popular, and got republished in several major news outlets (here, here, here, here (they get the number wrong) and more recently here), got a mention from the Minister of External Affairs & returns 29,100 results on Google. There's also plenty of references to it here on Reddit.
Patnaik is not the first to calculate such a figure. Angus Maddison thought it was £100 million, Simon Digby said £1 billion, Javier Estaban said £40 million see Roy (2019). The huge range of figures should set off some alarm bells.
So how did Patnaik calculate this (shockingly large) figure? Well, even though I don't have access to the festschrift, she conveniently has written an article detailing her methodology here. Let's have a look.
How exactly did the British manage to diddle us and drain our wealth’ ? was the question that Basudev Chatterjee (later editor of a volume in the Towards Freedom project) had posed to me 50 years ago when we were fellow-students abroad.
This is begging the question.
After decades of research I find that using India’s commodity export surplus as the measure and applying an interest rate of 5%, the total drain from 1765 to 1938, compounded up to 2016, comes to £9.2 trillion; since $4.86 exchanged for £1 those days, this sum equals about $45 trillion.
This is completely meaningless. To understand why it's meaningless consider India's annual coconut exports. These are almost certainly a surplus but the surplus in trade is countered by the other country buying the product (indeed, by definition, trade surpluses contribute to the GDP of a nation which hardly plays into intuitive conceptualisations of drain).
Furthermore, Dewey (2019) critiques the 5% interest rate.
She [Patnaik] consistently adopts statistical assumptions (such as compound interest at a rate of 5% per annum over centuries) that exaggerate the magnitude of the drain
Moving on:
The exact mechanism of drain, or transfers from India to Britain was quite simple.
Convenient.
Drain theory possessed the political merit of being easily grasped by a nation of peasants. [...] No other idea could arouse people than the thought that they were being taxed so that others in far off lands might live in comfort. [...] It was, therefore, inevitable that the drain theory became the main staple of nationalist political agitation during the Gandhian era.
- Chandra et al. (1989)
The key factor was Britain’s control over our taxation revenues combined with control over India’s financial gold and forex earnings from its booming commodity export surplus with the world. Simply put, Britain used locally raised rupee tax revenues to pay for its net import of goods, a highly abnormal use of budgetary funds not seen in any sovereign country.
The issue with figures like these is they all make certain methodological assumptions that are impossible to prove. From Roy in Frankema et al. (2019):
the "drain theory" of Indian poverty cannot be tested with evidence, for several reasons. First, it rests on the counterfactual that any money saved on account of factor payments abroad would translate into domestic investment, which can never be proved. Second, it rests on "the primitive notion that all payments to foreigners are "drain"", that is, on the assumption that these payments did not contribute to domestic national income to the equivalent extent (Kumar 1985, 384; see also Chaudhuri 1968). Again, this cannot be tested. [...] Fourth, while British officers serving India did receive salaries that were many times that of the average income in India, a paper using cross-country data shows that colonies with better paid officers were governed better (Jones 2013).
Indeed, drain theory rests on some very weak foundations. This, in of itself, should be enough to dismiss any of the other figures that get thrown out. Nonetheless, I felt it would be a useful exercise to continue exploring Patnaik's take on drain theory.
The East India Company from 1765 onwards allocated every year up to one-third of Indian budgetary revenues net of collection costs, to buy a large volume of goods for direct import into Britain, far in excess of that country’s own needs.
So what's going on here? Well Roy (2019) explains it better:
Colonial India ran an export surplus, which, together with foreign investment, was used to pay for services purchased from Britain. These payments included interest on public debt, salaries, and pensions paid to government offcers who had come from Britain, salaries of managers and engineers, guaranteed profts paid to railway companies, and repatriated business profts. How do we know that any of these payments involved paying too much? The answer is we do not.
So what was really happening is the government was paying its workers for services (as well as guaranteeing profits - to promote investment - something the GoI does today Dalal (2019), and promoting business in India), and those workers were remitting some of that money to Britain. This is hardly a drain (unless, of course, Indian diaspora around the world today are "draining" it). In some cases, the remittances would take the form of goods (as described) see Chaudhuri (1983):
It is obvious that these debit items were financed through the export surplus on merchandise account, and later, when railway construction started on a large scale in India, through capital import. Until 1833 the East India Company followed a cumbersome method in remitting the annual home charges. This was to purchase export commodities in India out of revenue, which were then shipped to London and the proceeds from their sale handed over to the home treasury.
While Roy's earlier point argues better paid officers governed better, it is honestly impossible to say what part of the repatriated export surplus was a drain, and what was not. However calling all of it a drain is definitely misguided.
It's worth noting that Patnaik seems to make no attempt to quantify the benefits of the Raj either, Dewey (2019)'s 2nd criticism:
she [Patnaik] consistently ignores research that would tend to cut the economic impact of the drain down to size, such as the work on the sources of investment during the industrial revolution (which shows that industrialisation was financed by the ploughed-back profits of industrialists) or the costs of empire school (which stresses the high price of imperial defence)

Since tropical goods were highly prized in other cold temperate countries which could never produce them, in effect these free goods represented international purchasing power for Britain which kept a part for its own use and re-exported the balance to other countries in Europe and North America against import of food grains, iron and other goods in which it was deficient.
Re-exports necessarily adds value to goods when the goods are processed and when the goods are transported. The country with the largest navy at the time would presumably be in very good stead to do the latter.
The British historians Phyllis Deane and WA Cole presented an incorrect estimate of Britain’s 18th-19th century trade volume, by leaving out re-exports completely. I found that by 1800 Britain’s total trade was 62% higher than their estimate, on applying the correct definition of trade including re-exports, that is used by the United Nations and by all other international organisations.
While interesting, and certainly expected for such an old book, re-exporting necessarily adds value to goods.
When the Crown took over from the Company, from 1861 a clever system was developed under which all of India’s financial gold and forex earnings from its fast-rising commodity export surplus with the world, was intercepted and appropriated by Britain. As before up to a third of India’s rising budgetary revenues was not spent domestically but was set aside as ‘expenditure abroad’.
So, what does this mean? Britain appropriated all of India's earnings, and then spent a third of it aboard? Not exactly. She is describing home charges see Roy (2019) again:
Some of the expenditures on defense and administration were made in sterling and went out of the country. This payment by the government was known as the Home Charges. For example, interest payment on loans raised to finance construction of railways and irrigation works, pensions paid to retired officers, and purchase of stores, were payments in sterling. [...] almost all money that the government paid abroad corresponded to the purchase of a service from abroad. [...] The balance of payments system that emerged after 1800 was based on standard business principles. India bought something and paid for it. State revenues were used to pay for wages of people hired abroad, pay for interest on loans raised abroad, and repatriation of profits on foreign investments coming into India. These were legitimate market transactions.
Indeed, if paying for what you buy is drain, then several billions of us are drained every day.
The Secretary of State for India in Council, based in London, invited foreign importers to deposit with him the payment (in gold, sterling and their own currencies) for their net imports from India, and these gold and forex payments disappeared into the yawning maw of the SoS’s account in the Bank of England.
It should be noted that India having two heads was beneficial, and encouraged investment per Roy (2019):
The fact that the India Office in London managed a part of the monetary system made India creditworthy, stabilized its currency, and encouraged foreign savers to put money into railways and private enterprise in India. Current research on the history of public debt shows that stable and large colonies found it easier to borrow abroad than independent economies because the investors trusted the guarantee of the colonist powers.

Against India’s net foreign earnings he issued bills, termed Council bills (CBs), to an equivalent rupee value. The rate (between gold-linked sterling and silver rupee) at which the bills were issued, was carefully adjusted to the last farthing, so that foreigners would never find it more profitable to ship financial gold as payment directly to Indians, compared to using the CB route. Foreign importers then sent the CBs by post or by telegraph to the export houses in India, that via the exchange banks were paid out of the budgeted provision of sums under ‘expenditure abroad’, and the exporters in turn paid the producers (peasants and artisans) from whom they sourced the goods.
Sunderland (2013) argues CBs had two main roles (and neither were part of a grand plot to keep gold out of India):
Council bills had two roles. They firstly promoted trade by handing the IO some control of the rate of exchange and allowing the exchange banks to remit funds to India and to hedge currency transaction risks. They also enabled the Indian government to transfer cash to England for the payment of its UK commitments.

The United Nations (1962) historical data for 1900 to 1960, show that for three decades up to 1928 (and very likely earlier too) India posted the second highest merchandise export surplus in the world, with USA in the first position. Not only were Indians deprived of every bit of the enormous international purchasing power they had earned over 175 years, even its rupee equivalent was not issued to them since not even the colonial government was credited with any part of India’s net gold and forex earnings against which it could issue rupees. The sleight-of-hand employed, namely ‘paying’ producers out of their own taxes, made India’s export surplus unrequited and constituted a tax-financed drain to the metropolis, as had been correctly pointed out by those highly insightful classical writers, Dadabhai Naoroji and RCDutt.
It doesn't appear that others appreciate their insight Roy (2019):
K. N. Chaudhuri rightly calls such practice ‘confused’ economics ‘coloured by political feelings’.

Surplus budgets to effect such heavy tax-financed transfers had a severe employment–reducing and income-deflating effect: mass consumption was squeezed in order to release export goods. Per capita annual foodgrains absorption in British India declined from 210 kg. during the period 1904-09, to 157 kg. during 1937-41, and to only 137 kg by 1946.
Dewey (1978) points out reliability issues with Indian agriculutural statistics, however this calorie decline persists to this day. Some of it is attributed to less food being consumed at home Smith (2015), a lower infectious disease burden Duh & Spears (2016) and diversified diets Vankatesh et al. (2016).
If even a part of its enormous foreign earnings had been credited to it and not entirely siphoned off, India could have imported modern technology to build up an industrial structure as Japan was doing.
This is, unfortunately, impossible to prove. Had the British not arrived in India, there is no clear indication that India would've united (this is arguably more plausible than the given counterfactual1). Had the British not arrived in India, there is no clear indication India would not have been nuked in WW2, much like Japan. Had the British not arrived in India, there is no clear indication India would not have been invaded by lizard people, much like Japan. The list continues eternally.
Nevertheless, I will charitably examine the given counterfactual anyway. Did pre-colonial India have industrial potential? The answer is a resounding no.
From Gupta (1980):
This article starts from the premise that while economic categories - the extent of commodity production, wage labour, monetarisation of the economy, etc - should be the basis for any analysis of the production relations of pre-British India, it is the nature of class struggles arising out of particular class alignments that finally gives the decisive twist to social change. Arguing on this premise, and analysing the available evidence, this article concludes that there was little potential for industrial revolution before the British arrived in India because, whatever might have been the character of economic categories of that period, the class relations had not sufficiently matured to develop productive forces and the required class struggle for a 'revolution' to take place.
A view echoed in Raychaudhuri (1983):
Yet all of this did not amount to an economic situation comparable to that of western Europe on the eve of the industrial revolution. Her technology - in agriculture as well as manufacturers - had by and large been stagnant for centuries. [...] The weakness of the Indian economy in the mid-eighteenth century, as compared to pre-industrial Europe was not simply a matter of technology and commercial and industrial organization. No scientific or geographical revolution formed part of the eighteenth-century Indian's historical experience. [...] Spontaneous movement towards industrialisation is unlikely in such a situation.
So now we've established India did not have industrial potential, was India similar to Japan just before the Meiji era? The answer, yet again, unsurprisingly, is no. Japan's economic situation was not comparable to India's, which allowed for Japan to finance its revolution. From Yasuba (1986):
All in all, the Japanese standard of living may not have been much below the English standard of living before industrialization, and both of them may have been considerably higher than the Indian standard of living. We can no longer say that Japan started from a pathetically low economic level and achieved a rapid or even "miraculous" economic growth. Japan's per capita income was almost as high as in Western Europe before industrialization, and it was possible for Japan to produce surplus in the Meiji Period to finance private and public capital formation.
The circumstances that led to Meiji Japan were extremely unique. See Tomlinson (1985):
Most modern comparisons between India and Japan, written by either Indianists or Japanese specialists, stress instead that industrial growth in Meiji Japan was the product of unique features that were not reproducible elsewhere. [...] it is undoubtably true that Japan's progress to industrialization has been unique and unrepeatable
So there you have it. Unsubstantiated statistical assumptions, calling any number you can a drain & assuming a counterfactual for no good reason gets you this $45 trillion number. Hopefully that's enough to bury it in the ground.
1. Several authors have affirmed that Indian identity is a colonial artefact. For example see Rajan 1969:
Perhaps the single greatest and most enduring impact of British rule over India is that it created an Indian nation, in the modern political sense. After centuries of rule by different dynasties overparts of the Indian sub-continent, and after about 100 years of British rule, Indians ceased to be merely Bengalis, Maharashtrians,or Tamils, linguistically and culturally.
or see Bryant 2000:
But then, it would be anachronistic to condemn eighteenth-century Indians, who served the British, as collaborators, when the notion of 'democratic' nationalism or of an Indian 'nation' did not then exist. [...] Indians who fought for them, differed from the Europeans in having a primary attachment to a non-belligerent religion, family and local chief, which was stronger than any identity they might have with a more remote prince or 'nation'.

Bibliography

Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press
Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian
Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost
Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian
Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice
Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times
Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan
Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times
Tuovila, Alicia (2019). Expenditure method. Investopedia
Dewey, Clive (2019). Changing the guard: The dissolution of the nationalist–Marxist orthodoxy in the agrarian and agricultural history of India. The Indian Economic & Social History Review
Chandra, Bipan et al. (1989). India's Struggle for Independence, 1857-1947. Penguin Books
Frankema, Ewout & Booth, Anne (2019). Fiscal Capacity and the Colonial State in Asia and Africa, c. 1850-1960. Cambridge University Press
Dalal, Sucheta (2019). IL&FS Controversy: Centre is Paying Up on Sovereign Guarantees to ADB, KfW for Group's Loan. TheWire
Chaudhuri, K.N. (1983). X - Foreign Trade and Balance of Payments (1757–1947). Cambridge University Press
Sunderland, David (2013). Financing the Raj: The City of London and Colonial India, 1858-1940. Boydell Press
Dewey, Clive (1978). Patwari and Chaukidar: Subordinate officials and the reliability of India’s agricultural statistics. Athlone Press
Smith, Lisa (2015). The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth. Food Policy
Duh, Josephine & Spears, Dean (2016). Health and Hunger: Disease, Energy Needs, and the Indian Calorie Consumption Puzzle. The Economic Journal
Vankatesh, P. et al. (2016). Relationship between Food Production and Consumption Diversity in India – Empirical Evidences from Cross Section Analysis. Agricultural Economics Research Review
Gupta, Shaibal (1980). Potential of Industrial Revolution in Pre-British India. Economic and Political Weekly
Raychaudhuri, Tapan (1983). I - The mid-eighteenth-century background. Cambridge University Press
Yasuba, Yasukichi (1986). Standard of Living in Japan Before Industrialization: From what Level did Japan Begin? A Comment. The Journal of Economic History
Tomblinson, B.R. (1985). Writing History Sideways: Lessons for Indian Economic Historians from Meiji Japan. Cambridge University Press
Rajan, M.S. (1969). The Impact of British Rule in India. Journal of Contemporary History
Bryant, G.J. (2000). Indigenous Mercenaries in the Service of European Imperialists: The Case of the Sepoys in the Early British Indian Army, 1750-1800. War in History
submitted by GaslightEveryone to u/GaslightEveryone [link] [comments]

on the fakeness of the internet

funny to see that subject pop up again. it was what drove me insane enough to find this sub in the first place.
at any rate, the problem is not the bots. I thought it was, but those are just part of the parasitic ecosystem.
but to get that, first we need to take a few steps back on web history, ad serving, UX, tracking technology and media advertising.
too lazy to gather links, but you know, do your googlin'.
I assume that most of you are fairly web literate here, but I'll try to go down into the bare bones as much as possible for those who aren't.
so let's start with a basic question - what is a web visitor anyway?
from the standpoint of a normal person, that would be a person browsing a given website or piece of content. from the standpoint of technology however all you know is that some device has downloaded content from your server using the http protocol. thanks to the wonderful technology of web browsers, you can plant browser cookies on a visitor - stuff that's used to remember if they logged in, what their preferences are, stuff that your service can read from the device. it also serves usually very basic telemetry like last visit time, session time, and so on.
this, over time has evolved in what we call browser fingerprinting, a convoluted bunch of technology that allows websites and web services to uniquely identify you.
it still doesn't know if you're a human or not, but from the standpoint of the web technology, you're a visitor.
now back in ye old days of the web, when the first banner ads were springing up, these were important questions. most consumers were still to be reached on traditional media channels, and ad spend would have to be justified somehow on the risky ventures of online business. so beyond traditional polls that would infer the value of visitors, websites would start tracking number of visitors, time on page and so on. these were used to milk the advertising cow so to speak, and it gave in to some funny developments like the creation of the popup ad - if I recon correctly on geocities, where they would just but the ads everywhere until some big auto company noticed that they're appearing on porn sites. so - put the ad in the popup, and you can claim it's not in the context of porn!
around this point in time the online ad business is still pretty low tech. you actually have to call a physical human being, they send you ppts and pdfs, you send back image files and excel sheets, you wire money, the ads run, and so on. this is called direct sales, and it's tracked again by counting a bunch of visitors, and telling you how much impressions and clicks your marvelous creatives and ad budget generated.
now enter google - or more precisely, a technology firm called doubleclick that was to be acquired by google. they developed a tool for automatic ad serving, later to be called programmatic advertising, that keeps the pesky sales dude out of the loop and achieves reasonable amounts of scale for a more hefty price - after all, if the sales are automated, you get a bidding war for attention between different advertisers, and you're paying for clicks.
so you can see how this was a strategic move for google - they already had the most valuable data available in this situation. they were seeing in real time what people were searching for, and using the programmatic ad serving system, you could effectively bid not just for general attention - but for attention with an intent to buy.
...and the way that google got this data is because they indexed the web, using bots. at least GoogleBot would identify itself as a site visitor, but in the meantime they developed a service for websites to comprehensively track their own visitors and where they were coming from and what they were doing on your website. incidentally, you could also put on google's ads on your webpage to earn quite a bit of money, as content relevant ads would be shown through the doubleclick system.
this kicked off two things:
one, the ability to classify your website visitors into different clusters and segments allowed businesses to start tailoring the appearance of the website or service to fit that specific audience segment, starting off the great fracture - segmentation of the web (in the sense that two people viewing the same website at the same time were not seeing the same thing)
two, it created a very strong financial incentive for people to trick google into thinking they were having actual human visitors that would click on ads, when in fact they were bots. in an even funnier twist, some of them were from browser hijackers, commonly known as malware at the time, which google cross-financed. look up download valley and crossrider.
at the cross section of the above two, you had one interesting twist: websites that would appear differently to the security bots or the compliance officers of Google as they would to fake visitors or malware jacked human beings. the former would get a benign looking website, while the latter would get bombarded with auto clicking ads.
this kicked off the billion dollar arms race called online advertising fraud.
I'm not here to shed a tear for big money corps bleeding money. the real fallout lay somewhere else, but for that you have to understand that you never really saw the real internet, you only saw your corner and the one that was personalized for you.
but if you ever had the pleasure of watching daytime TVs or off channels and witnessing the ads, you could kind of infer what kind of audience must be watching these shows generally. from quite clear rip offs to magic number lotteries and television fortune telling, these sorts of programming was aimed at the most gullible, bought for pennies, where the smallest audience portion had to be converted into a money making operation.
...and with audience segmentation and data gathering, that was now possible at unprecedented scale, automatically. so big was the scale in fact, that it gave birth to an entire new beast of an industry called affiliate marketing, where instead of a regular payroll, you'd get a cut of the sale should you figure out an angle on where to push whatever fucking bullshit the vendors were offering to whoever the fuck would be dumb enough to click on an ad and buy. (the funniest story I recall was someone pulling five figures a month because he figured out that if you buy ads on anime-hentai pages and sell PUA shit courses and e-books you'd make a killing)
at any rate, affiliate marketing brought with it the killer landing page, the thing that's supposed to hammer the nail in the coffin once you get through the banner ad. the earliest form of deceptiveness in memory comes from various pirate sites, that had fake download buttons as banner ads and virus alerts as the landing pages. but then at some point, some schmuck realized that for certain type of products, like diet pills or forex trading or whatever, the best lander is in fact a fake news page that comes packed with comments and all. that would convert like crazy, because it had the appearance of social proof.
until at least the lawsuits came raining down, and these sorts of landing pages and campaigns for being banned left right and centre on all platforms. which just launched a new arms race as the campaigns would be disguised for the bots doing the checkups, and aged facebook profiles would start selling for like 5K USD - these people were making 30-40k a day, they could afford to spend that much to continue running the shop.
speaking of facebook - it came just about the right time for the shit to brew max total. first they were unprecedented in the amount of data they were getting off of their users, and they came just in time to catch the full swing of what we call the 'responsive web' - that no user at the same time would see the same thing on their page, it was all allocated through an intricate web of recommendations, running real time, based on previously gathered and forecast behavioral data.
it also ran on one simple premise: take over the starting page position from google for most people, then they do not have to justify, ever, any ad spend that takes place on their platform, as long as it performs. furthermore, it was completely lacking any revenue share sort of scheme (save for the short period of facebook gaming, see Zynga), thus there was no incentive for the amount of bot traffic that the previous internet era had bred. instead, it came with an entirely different one - bots that would offer social proof in the way of shares and likes, but would not directly risk the business model, thus giving no incentive for facebook to fight them. (note that google didn't do much jack shit either besides indiscriminately penalizing websites it deemed suspicious when they reached critical payout thresholds)
the rest of the story you kind of sort of know. how the obama campaign was brilliant in using the new social media to inspire hope and blah blah blah, kicking the door open for big money politics who could hire the best snake oil salesmen in the market, who had the data and as you can see from the above, had the ethical standards of a shoe. at around 2014-2015 the press (the mainstream media) started to raise question about the duopoly, the buzzword of filter bubbles started appearing, not entirely unrelated to the fact that facebook by this time cannibalized their traffic with a fucking embedded share / like button and started charging money for them to reach their own audience. after 2016 the cries of fake news were everywhere, because there was no online space left which everyone was viewing the same way, and you had no way to verify what the person next to you was looking at.
since then, we've all become grandpa yelling at the television set, with nobody around us seeing what we're seeing on the screen, so we're being accused as bots and looking for bots under the carpet.
but it's been a long way coming, and the bots are honestly the least of our worries. trust me, I went bankrupt over that one. truth or fake doesn't even begin to describe the magnitude of the problem: more like we entered the phase where every word, event or picture is defined by who ever the fuck wins the auction over it, as the marketers of human attention grind the gears of the money mill without even understanding how fast they're digging towards hell.
don't believe me? look around the marketing and advertising related subs these days. the priests are eating the indulgences, and we're only now entering the period of deep fakes, good algo generated audio and good enough NLP. and in the meantime, the shadowrunners running up between two corp headquarter-highrises are skinning your belief systems.
so the best you can do is really, not litter the remnants of cyberspace which are not being mined, astroturfed or being pulled apart by the algos. no human connections on a nuclear trash heap mate.
submitted by gergo_v to sorceryofthespectacle [link] [comments]

Hibiscus Petroleum Berhad (5199.KL)


https://preview.redd.it/gp18bjnlabr41.jpg?width=768&format=pjpg&auto=webp&s=6054e7f52e8d52da403016139ae43e0e799abf15
Download PDF of this article here: https://docdro.id/6eLgUPo
In light of the recent fall in oil prices due to the Saudi-Russian dispute and dampening demand for oil due to the lockdowns implemented globally, O&G stocks have taken a severe beating, falling approximately 50% from their highs at the beginning of the year. Not spared from this onslaught is Hibiscus Petroleum Berhad (Hibiscus), a listed oil and gas (O&G) exploration and production (E&P) company.
Why invest in O&G stocks in this particularly uncertain period? For one, valuations of these stocks have fallen to multi-year lows, bringing the potential ROI on these stocks to attractive levels. Oil prices are cyclical, and are bound to return to the mean given a sufficiently long time horizon. The trick is to find those companies who can survive through this downturn and emerge into “normal” profitability once oil prices rebound.
In this article, I will explore the upsides and downsides of investing in Hibiscus. I will do my best to cater this report to newcomers to the O&G industry – rather than address exclusively experts and veterans of the O&G sector. As an equity analyst, I aim to provide a view on the company primarily, and will generally refrain from providing macro views on oil or opinions about secular trends of the sector. I hope you enjoy reading it!
Stock code: 5199.KL
Stock name: Hibiscus Petroleum Berhad
Financial information and financial reports: https://www.malaysiastock.biz/Corporate-Infomation.aspx?securityCode=5199
Company website: https://www.hibiscuspetroleum.com/

Company Snapshot

Hibiscus Petroleum Berhad (5199.KL) is an oil and gas (O&G) upstream exploration and production (E&P) company located in Malaysia. As an E&P company, their business can be basically described as:
· looking for oil,
· drawing it out of the ground, and
· selling it on global oil markets.
This means Hibiscus’s profits are particularly exposed to fluctuating oil prices. With oil prices falling to sub-$30 from about $60 at the beginning of the year, Hibiscus’s stock price has also fallen by about 50% YTD – from around RM 1.00 to RM 0.45 (as of 5 April 2020).
https://preview.redd.it/3dqc4jraabr41.png?width=641&format=png&auto=webp&s=7ba0e8614c4e9d781edfc670016a874b90560684
https://preview.redd.it/lvdkrf0cabr41.png?width=356&format=png&auto=webp&s=46f250a713887b06986932fa475dc59c7c28582e
While the company is domiciled in Malaysia, its two main oil producing fields are located in both Malaysia and the UK. The Malaysian oil field is commonly referred to as the North Sabah field, while the UK oil field is commonly referred to as the Anasuria oil field. Hibiscus has licenses to other oil fields in different parts of the world, notably the Marigold/Sunflower oil fields in the UK and the VIC cluster in Australia, but its revenues and profits mainly stem from the former two oil producing fields.
Given that it’s a small player and has only two primary producing oil fields, it’s not surprising that Hibiscus sells its oil to a concentrated pool of customers, with 2 of them representing 80% of its revenues (i.e. Petronas and BP). Fortunately, both these customers are oil supermajors, and are unlikely to default on their obligations despite low oil prices.
At RM 0.45 per share, the market capitalization is RM 714.7m and it has a trailing PE ratio of about 5x. It doesn’t carry any debt, and it hasn’t paid a dividend in its listing history. The MD, Mr. Kenneth Gerard Pereira, owns about 10% of the company’s outstanding shares.

Reserves (Total recoverable oil) & Production (bbl/day)

To begin analyzing the company, it’s necessary to understand a little of the industry jargon. We’ll start with Reserves and Production.
In general, there are three types of categories for a company’s recoverable oil volumes – Reserves, Contingent Resources and Prospective Resources. Reserves are those oil fields which are “commercial”, which is defined as below:
As defined by the SPE PRMS, Reserves are “… quantities of petroleum anticipated to be commercially recoverable by application of development projects to known accumulations from a given date forward under defined conditions.” Therefore, Reserves must be discovered (by drilling, recoverable (with current technology), remaining in the subsurface (at the effective date of the evaluation) and “commercial” based on the development project proposed.)
Note that Reserves are associated with development projects. To be considered as “commercial”, there must be a firm intention to proceed with the project in a reasonable time frame (typically 5 years, and such intention must be based upon all of the following criteria:)
- A reasonable assessment of the future economics of the development project meeting defined investment and operating criteria; - A reasonable expectation that there will be a market for all or at least the expected sales quantities of production required to justify development; - Evidence that the necessary production and transportation facilities are available or can be made available; and - Evidence that legal, contractual, environmental and other social and economic concerns will allow for the actual implementation of the recovery project being evaluated.
Contingent Resources and Prospective Resources are further defined as below:
- Contingent Resources: potentially recoverable volumes associated with a development plan that targets discovered volumes but is not (yet commercial (as defined above); and) - Prospective Resources: potentially recoverable volumes associated with a development plan that targets as yet undiscovered volumes.
In the industry lingo, we generally refer to Reserves as ‘P’ and Contingent Resources as ‘C’. These ‘P’ and ‘C’ resources can be further categorized into 1P/2P/3P resources and 1C/2C/3C resources, each referring to a low/medium/high estimate of the company’s potential recoverable oil volumes:
- Low/1C/1P estimate: there should be reasonable certainty that volumes actually recovered will equal or exceed the estimate; - Best/2C/2P estimate: there should be an equal likelihood of the actual volumes of petroleum being larger or smaller than the estimate; and - High/3C/3P estimate: there is a low probability that the estimate will be exceeded.
Hence in the E&P industry, it is easy to see why most investors and analysts refer to the 2P estimate as the best estimate for a company’s actual recoverable oil volumes. This is because 2P reserves (‘2P’ referring to ‘Proved and Probable’) are a middle estimate of the recoverable oil volumes legally recognized as “commercial”.
However, there’s nothing stopping you from including 2C resources (riskier) or utilizing 1P resources (conservative) as your estimate for total recoverable oil volumes, depending on your risk appetite. In this instance, the company has provided a snapshot of its 2P and 2C resources in its analyst presentation:
https://preview.redd.it/o8qejdyc8br41.png?width=710&format=png&auto=webp&s=b3ab9be8f83badf0206adc982feda3a558d43e78
Basically, what the company is saying here is that by 2021, it will have classified as 2P reserves at least 23.7 million bbl from its Anasuria field and 20.5 million bbl from its North Sabah field – for total 2P reserves of 44.2 million bbl (we are ignoring the Australian VIC cluster as it is only estimated to reach first oil by 2022).
Furthermore, the company is stating that they have discovered (but not yet legally classified as “commercial”) a further 71 million bbl of oil from both the Anasuria and North Sabah fields, as well as the Marigold/Sunflower fields. If we include these 2C resources, the total potential recoverable oil volumes could exceed 100 million bbl.
In this report, we shall explore all valuation scenarios giving consideration to both 2P and 2C resources.
https://preview.redd.it/gk54qplf8br41.png?width=489&format=png&auto=webp&s=c905b7a6328432218b5b9dfd53cc9ef1390bd604
The company further targets a 2021 production rate of 20,000 bbl (LTM: 8,000 bbl), which includes 5,000 bbl from its Anasuria field (LTM: 2,500 bbl) and 7,000 bbl from its North Sabah field (LTM: 5,300 bbl).
This is a substantial increase in forecasted production from both existing and prospective oil fields. If it materializes, annual production rate could be as high as 7,300 mmbbl, and 2021 revenues (given FY20 USD/bbl of $60) could exceed RM 1.5 billion (FY20: RM 988 million).
However, this targeted forecast is quite a stretch from current production levels. Nevertheless, we shall consider all provided information in estimating a valuation for Hibiscus.
To understand Hibiscus’s oil production capacity and forecast its revenues and profits, we need to have a better appreciation of the performance of its two main cash-generating assets – the North Sabah field and the Anasuria field.

North Sabah oil field
https://preview.redd.it/62nssexj8br41.png?width=1003&format=png&auto=webp&s=cd78f86d51165fb9a93015e49496f7f98dad64dd
Hibiscus owns a 50% interest in the North Sabah field together with its partner Petronas, and has production rights over the field up to year 2040. The asset contains 4 oil fields, namely the St Joseph field, South Furious field, SF 30 field and Barton field.
For the sake of brevity, we shall not delve deep into the operational aspects of the fields or the contractual nature of its production sharing contract (PSC). We’ll just focus on the factors which relate to its financial performance. These are:
· Average uptime
· Total oil sold
· Average realized oil price
· Average OPEX per bbl
With regards to average uptime, we can see that the company maintains relative high facility availability, exceeding 90% uptime in all quarters of the LTM with exception of Jul-Sep 2019. The dip in average uptime was due to production enhancement projects and maintenance activities undertaken to improve the production capacity of the St Joseph and SF30 oil fields.
Hence, we can conclude that management has a good handle on operational performance. It also implies that there is little room for further improvement in production resulting from increased uptime.
As North Sabah is under a production sharing contract (PSC), there is a distinction between gross oil production and net oil production. The former relates to total oil drawn out of the ground, whereas the latter refers to Hibiscus’s share of oil production after taxes, royalties and expenses are accounted for. In this case, we want to pay attention to net oil production, not gross.
We can arrive at Hibiscus’s total oil sold for the last twelve months (LTM) by adding up the total oil sold for each of the last 4 quarters. Summing up the figures yields total oil sold for the LTM of approximately 2,075,305 bbl.
Then, we can arrive at an average realized oil price over the LTM by averaging the average realized oil price for the last 4 quarters, giving us an average realized oil price over the LTM of USD 68.57/bbl. We can do the same for average OPEX per bbl, giving us an average OPEX per bbl over the LTM of USD 13.23/bbl.
Thus, we can sum up the above financial performance of the North Sabah field with the following figures:
· Total oil sold: 2,075,305 bbl
· Average realized oil price: USD 68.57/bbl
· Average OPEX per bbl: USD 13.23/bbl

Anasuria oil field
https://preview.redd.it/586u4kfo8br41.png?width=1038&format=png&auto=webp&s=7580fc7f7df7e948754d025745a5cf47d4393c0f
Doing the same exercise as above for the Anasuria field, we arrive at the following financial performance for the Anasuria field:
· Total oil sold: 1,073,304 bbl
· Average realized oil price: USD 63.57/bbl
· Average OPEX per bbl: USD 23.22/bbl
As gas production is relatively immaterial, and to be conservative, we shall only consider the crude oil production from the Anasuria field in forecasting revenues.

Valuation (Method 1)

Putting the figures from both oil fields together, we get the following data:
https://preview.redd.it/7y6064dq8br41.png?width=700&format=png&auto=webp&s=2a4120563a011cf61fc6090e1cd5932602599dc2
Given that we have determined LTM EBITDA of RM 632m, the next step would be to subtract ITDA (interest, tax, depreciation & amortization) from it to obtain estimated LTM Net Profit. Using FY2020’s ITDA of approximately RM 318m as a guideline, we arrive at an estimated LTM Net Profit of RM 314m (FY20: 230m). Given the current market capitalization of RM 714.7m, this implies a trailing LTM PE of 2.3x.
Performing a sensitivity analysis given different oil prices, we arrive at the following net profit table for the company under different oil price scenarios, assuming oil production rate and ITDA remain constant:
https://preview.redd.it/xixge5sr8br41.png?width=433&format=png&auto=webp&s=288a00f6e5088d01936f0217ae7798d2cfcf11f2
From the above exercise, it becomes apparent that Hibiscus has a breakeven oil price of about USD 41.8863/bbl, and has a lot of operating leverage given the exponential rate of increase in its Net Profit with each consequent increase in oil prices.
Considering that the oil production rate (EBITDA) is likely to increase faster than ITDA’s proportion to revenues (fixed costs), at an implied PE of 4.33x, it seems likely that an investment in Hibiscus will be profitable over the next 10 years (with the assumption that oil prices will revert to the mean in the long-term).

Valuation (Method 2)

Of course, there are a lot of assumptions behind the above method of valuation. Hence, it would be prudent to perform multiple methods of valuation and compare the figures to one another.
As opposed to the profit/loss assessment in Valuation (Method 1), another way of performing a valuation would be to estimate its balance sheet value, i.e. total revenues from 2P Reserves, and assign a reasonable margin to it.
https://preview.redd.it/o2eiss6u8br41.png?width=710&format=png&auto=webp&s=03960cce698d9cedb076f3d5f571b3c59d908fa8
From the above, we understand that Hibiscus’s 2P reserves from the North Sabah and Anasuria fields alone are approximately 44.2 mmbbl (we ignore contribution from Australia’s VIC cluster as it hasn’t been developed yet).
Doing a similar sensitivity analysis of different oil prices as above, we arrive at the following estimated total revenues and accumulated net profit:
https://preview.redd.it/h8hubrmw8br41.png?width=450&format=png&auto=webp&s=6d23f0f9c3dafda89e758b815072ba335467f33e
Let’s assume that the above average of RM 9.68 billion in total realizable revenues from current 2P reserves holds true. If we assign a conservative Net Profit margin of 15% (FY20: 23%; past 5 years average: 16%), we arrive at estimated accumulated Net Profit from 2P Reserves of RM 1.452 billion. Given the current market capitalization of RM 714 million, we might be able to say that the equity is worth about twice the current share price.
However, it is understandable that some readers might feel that the figures used in the above estimate (e.g. net profit margin of 15%) were randomly plucked from the sky. So how do we reconcile them with figures from the financial statements? Fortunately, there appears to be a way to do just that.
Intangible Assets
I refer you to a figure in the financial statements which provides a shortcut to the valuation of 2P Reserves. This is the carrying value of Intangible Assets on the Balance Sheet.
As of 2QFY21, that amount was RM 1,468,860,000 (i.e. RM 1.468 billion).
https://preview.redd.it/hse8ttb09br41.png?width=881&format=png&auto=webp&s=82e48b5961c905fe9273cb6346368de60202ebec
Quite coincidentally, one might observe that this figure is dangerously close to the estimated accumulated Net Profit from 2P Reserves of RM 1.452 billion we calculated earlier. But why would this amount matter at all?
To answer that, I refer you to the notes of the Annual Report FY20 (AR20). On page 148 of the AR20, we find the following two paragraphs:
E&E assets comprise of rights and concession and conventional studies. Following the acquisition of a concession right to explore a licensed area, the costs incurred such as geological and geophysical surveys, drilling, commercial appraisal costs and other directly attributable costs of exploration and appraisal including technical and administrative costs, are capitalised as conventional studies, presented as intangible assets.
E&E assets are assessed for impairment when facts and circumstances suggest that the carrying amount of an E&E asset may exceed its recoverable amount. The Group will allocate E&E assets to cash generating unit (“CGU”s or groups of CGUs for the purpose of assessing such assets for impairment. Each CGU or group of units to which an E&E asset is allocated will not be larger than an operating segment as disclosed in Note 39 to the financial statements.)
Hence, we can determine that firstly, the intangible asset value represents capitalized costs of acquisition of the oil fields, including technical exploration costs and costs of acquiring the relevant licenses. Secondly, an impairment review will be carried out when “the carrying amount of an E&E asset may exceed its recoverable amount”, with E&E assets being allocated to “cash generating units” (CGU) for the purposes of assessment.
On page 169 of the AR20, we find the following:
Carrying amounts of the Group’s intangible assets, oil and gas assets and FPSO are reviewed for possible impairment annually including any indicators of impairment. For the purpose of assessing impairment, assets are grouped at the lowest level CGUs for which there is a separately identifiable cash flow available. These CGUs are based on operating areas, represented by the 2011 North Sabah EOR PSC (“North Sabah”, the Anasuria Cluster, the Marigold and Sunflower fields, the VIC/P57 exploration permit (“VIC/P57”) and the VIC/L31 production license (“VIC/L31”).)
So apparently, the CGUs that have been assigned refer to the respective oil producing fields, two of which include the North Sabah field and the Anasuria field. In order to perform the impairment review, estimates of future cash flow will be made by management to assess the “recoverable amount” (as described above), subject to assumptions and an appropriate discount rate.
Hence, what we can gather up to now is that management will estimate future recoverable cash flows from a CGU (i.e. the North Sabah and Anasuria oil fields), compare that to their carrying value, and perform an impairment if their future recoverable cash flows are less than their carrying value. In other words, if estimated accumulated profits from the North Sabah and Anasuria oil fields are less than their carrying value, an impairment is required.
So where do we find the carrying values for the North Sabah and Anasuria oil fields? Further down on page 184 in the AR20, we see the following:
Included in rights and concession are the carrying amounts of producing field licenses in the Anasuria Cluster amounting to RM668,211,518 (2018: RM687,664,530, producing field licenses in North Sabah amounting to RM471,031,008 (2018: RM414,333,116))
Hence, we can determine that the carrying values for the North Sabah and Anasuria oil fields are RM 471m and RM 668m respectively. But where do we find the future recoverable cash flows of the fields as estimated by management, and what are the assumptions used in that calculation?
Fortunately, we find just that on page 185:
17 INTANGIBLE ASSETS (CONTINUED)
(a Anasuria Cluster)
The Directors have concluded that there is no impairment indicator for Anasuria Cluster during the current financial year. In the previous financial year, due to uncertainties in crude oil prices, the Group has assessed the recoverable amount of the intangible assets, oil and gas assets and FPSO relating to the Anasuria Cluster. The recoverable amount is determined using the FVLCTS model based on discounted cash flows (“DCF” derived from the expected cash in/outflow pattern over the production lives.)
The key assumptions used to determine the recoverable amount for the Anasuria Cluster were as follows:
(i Discount rate of 10%;)
(ii Future cost inflation factor of 2% per annum;)
(iii Oil price forecast based on the oil price forward curve from independent parties; and,)
(iv Oil production profile based on the assessment by independent oil and gas reserve experts.)
Based on the assessments performed, the Directors concluded that the recoverable amount calculated based on the valuation model is higher than the carrying amount.
(b North Sabah)
The acquisition of the North Sabah assets was completed in the previous financial year. Details of the acquisition are as disclosed in Note 15 to the financial statements.
The Directors have concluded that there is no impairment indicator for North Sabah during the current financial year.
Here, we can see that the recoverable amount of the Anasuria field was estimated based on a DCF of expected future cash flows over the production life of the asset. The key assumptions used by management all seem appropriate, including a discount rate of 10% and oil price and oil production estimates based on independent assessment. From there, management concludes that the recoverable amount of the Anasuria field is higher than its carrying amount (i.e. no impairment required). Likewise, for the North Sabah field.
How do we interpret this? Basically, what management is saying is that given a 10% discount rate and independent oil price and oil production estimates, the accumulated profits (i.e. recoverable amount) from both the North Sabah and the Anasuria fields exceed their carrying amounts of RM 471m and RM 668m respectively.
In other words, according to management’s own estimates, the carrying value of the Intangible Assets of RM 1.468 billion approximates the accumulated Net Profit recoverable from 2P reserves.
To conclude Valuation (Method 2), we arrive at the following:

Our estimates Management estimates
Accumulated Net Profit from 2P Reserves RM 1.452 billion RM 1.468 billion

Financials

By now, we have established the basic economics of Hibiscus’s business, including its revenues (i.e. oil production and oil price scenarios), costs (OPEX, ITDA), profitability (breakeven, future earnings potential) and balance sheet value (2P reserves, valuation). Moving on, we want to gain a deeper understanding of the 3 statements to anticipate any blind spots and risks. We’ll refer to the financial statements of both the FY20 annual report and the 2Q21 quarterly report in this analysis.
For the sake of brevity, I’ll only point out those line items which need extra attention, and skip over the rest. Feel free to go through the financial statements on your own to gain a better familiarity of the business.
https://preview.redd.it/h689bss79br41.png?width=810&format=png&auto=webp&s=ed47fce6a5c3815dd3d4f819e31f1ce39ccf4a0b
Income Statement
First, we’ll start with the Income Statement on page 135 of the AR20. Revenues are straightforward, as we’ve discussed above. Cost of Sales and Administrative Expenses fall under the jurisdiction of OPEX, which we’ve also seen earlier. Other Expenses are mostly made up of Depreciation & Amortization of RM 115m.
Finance Costs are where things start to get tricky. Why does a company which carries no debt have such huge amounts of finance costs? The reason can be found in Note 8, where it is revealed that the bulk of finance costs relate to the unwinding of discount of provision for decommissioning costs of RM 25m (Note 32).
https://preview.redd.it/4omjptbe9br41.png?width=1019&format=png&auto=webp&s=eaabfc824134063100afa62edfd36a34a680fb60
This actually refers to the expected future costs of restoring the Anasuria and North Sabah fields to their original condition once the oil reserves have been depleted. Accounting standards require the company to provide for these decommissioning costs as they are estimable and probable. The way the decommissioning costs are accounted for is the same as an amortized loan, where the initial carrying value is recognized as a liability and the discount rate applied is reversed each year as an expense on the Income Statement. However, these expenses are largely non-cash in nature and do not necessitate a cash outflow every year (FY20: RM 69m).
Unwinding of discount on non-current other payables of RM 12m relate to contractual payments to the North Sabah sellers. We will discuss it later.
Taxation is another tricky subject, and is even more significant than Finance Costs at RM 161m. In gist, Hibiscus is subject to the 38% PITA (Petroleum Income Tax Act) under Malaysian jurisdiction, and the 30% Petroleum tax + 10% Supplementary tax under UK jurisdiction. Of the RM 161m, RM 41m of it relates to deferred tax which originates from the difference between tax treatment and accounting treatment on capitalized assets (accelerated depreciation vs straight-line depreciation). Nonetheless, what you should take away from this is that the tax expense is a tangible expense and material to breakeven analysis.
Fortunately, tax is a variable expense, and should not materially impact the cash flow of Hibiscus in today’s low oil price environment.
Note: Cash outflows for Tax Paid in FY20 was RM 97m, substantially below the RM 161m tax expense.
https://preview.redd.it/1xrnwzm89br41.png?width=732&format=png&auto=webp&s=c078bc3e18d9c79d9a6fbe1187803612753f69d8
Balance Sheet
The balance sheet of Hibiscus is unexciting; I’ll just bring your attention to those line items which need additional scrutiny. I’ll use the figures in the latest 2Q21 quarterly report (2Q21) and refer to the notes in AR20 for clarity.
We’ve already discussed Intangible Assets in the section above, so I won’t dwell on it again.
Moving on, the company has Equipment of RM 582m, largely relating to O&G assets (e.g. the Anasuria FPSO vessel and CAPEX incurred on production enhancement projects). Restricted cash and bank balances represent contractual obligations for decommissioning costs of the Anasuria Cluster, and are inaccessible for use in operations.
Inventories are relatively low, despite Hibiscus being an E&P company, so forex fluctuations on carrying value of inventories are relatively immaterial. Trade receivables largely relate to entitlements from Petronas and BP (both oil supermajors), and are hence quite safe from impairment. Other receivables, deposits and prepayments are significant as they relate to security deposits placed with sellers of the oil fields acquired; these should be ignored for cash flow purposes.
Note: Total cash and bank balances do not include approximately RM 105 m proceeds from the North Sabah December 2019 offtake (which was received in January 2020)
Cash and bank balances of RM 90m do not include RM 105m of proceeds from offtake received in 3Q21 (Jan 2020). Hence, the actual cash and bank balances as of 2Q21 approximate RM 200m.
Liabilities are a little more interesting. First, I’ll draw your attention to the significant Deferred tax liabilities of RM 457m. These largely relate to the amortization of CAPEX (i.e. Equipment and capitalized E&E expenses), which is given an accelerated depreciation treatment for tax purposes.
The way this works is that the government gives Hibiscus a favorable tax treatment on capital expenditures incurred via an accelerated depreciation schedule, so that the taxable income is less than usual. However, this leads to the taxable depreciation being utilized quicker than accounting depreciation, hence the tax payable merely deferred to a later period – when the tax depreciation runs out but accounting depreciation remains. Given the capital intensive nature of the business, it is understandable why Deferred tax liabilities are so large.
We’ve discussed Provision for decommissioning costs under the Finance Costs section earlier. They are also quite significant at RM 266m.
Notably, the Other Payables and Accruals are a hefty RM 431m. What do they relate to? Basically, they are contractual obligations to the sellers of the oil fields which are only payable upon oil prices reaching certain thresholds. Hence, while they are current in nature, they will only become payable when oil prices recover to previous highs, and are hence not an immediate cash outflow concern given today’s low oil prices.
Cash Flow Statement
There is nothing in the cash flow statement which warrants concern.
Notably, the company generated OCF of approximately RM 500m in FY20 and RM 116m in 2Q21. It further incurred RM 330m and RM 234m of CAPEX in FY20 and 2Q21 respectively, largely owing to production enhancement projects to increase the production rate of the Anasuria and North Sabah fields, which according to management estimates are accretive to ROI.
Tax paid was RM 97m in FY20 and RM 61m in 2Q21 (tax expense: RM 161m and RM 62m respectively).

Risks

There are a few obvious and not-so-obvious risks that one should be aware of before investing in Hibiscus. We shall not consider operational risks (e.g. uptime, OPEX) as they are outside the jurisdiction of the equity analyst. Instead, we shall focus on the financial and strategic risks largely outside the control of management. The main ones are:
· Oil prices remaining subdued for long periods of time
· Fluctuation of exchange rates
· Customer concentration risk
· 2P Reserves being less than estimated
· Significant current and non-current liabilities
· Potential issuance of equity
Oil prices remaining subdued
Of topmost concern in the minds of most analysts is whether Hibiscus has the wherewithal to sustain itself through this period of low oil prices (sub-$30). A quick and dirty estimate of annual cash outflow (i.e. burn rate) assuming a $20 oil world and historical production rates is between RM 50m-70m per year, which considering the RM 200m cash balance implies about 3-4 years of sustainability before the company runs out of cash and has to rely on external assistance for financing.
Table 1: Hibiscus EBITDA at different oil price and exchange rates
https://preview.redd.it/gxnekd6h9br41.png?width=670&format=png&auto=webp&s=edbfb9621a43480d11e3b49de79f61a6337b3d51
The above table shows different EBITDA scenarios (RM ‘m) given different oil prices (left column) and USD:MYR exchange rates (top row). Currently, oil prices are $27 and USD:MYR is 1:4.36.
Given conservative assumptions of average OPEX/bbl of $20 (current: $15), we can safely say that the company will be loss-making as long as oil remains at $20 or below (red). However, we can see that once oil prices hit $25, the company can tank the lower-end estimate of the annual burn rate of RM 50m (orange), while at RM $27 it can sufficiently muddle through the higher-end estimate of the annual burn rate of RM 70m (green).
Hence, we can assume that as long as the average oil price over the next 3-4 years remains above $25, Hibiscus should come out of this fine without the need for any external financing.
Customer Concentration Risk
With regards to customer concentration risk, there is not much the analyst or investor can do except to accept the risk. Fortunately, 80% of revenues can be attributed to two oil supermajors (Petronas and BP), hence the risk of default on contractual obligations and trade receivables seems to be quite diminished.
2P Reserves being less than estimated
2P Reserves being less than estimated is another risk that one should keep in mind. Fortunately, the current market cap is merely RM 714m – at half of estimated recoverable amounts of RM 1.468 billion – so there’s a decent margin of safety. In addition, there are other mitigating factors which shall be discussed in the next section (‘Opportunities’).
Significant non-current and current liabilities
The significant non-current and current liabilities have been addressed in the previous section. It has been determined that they pose no threat to immediate cash flow due to them being long-term in nature (e.g. decommissioning costs, deferred tax, etc). Hence, for the purpose of assessing going concern, their amounts should not be a cause for concern.
Potential issuance of equity
Finally, we come to the possibility of external financing being required in this low oil price environment. While the company should last 3-4 years on existing cash reserves, there is always the risk of other black swan events materializing (e.g. coronavirus) or simply oil prices remaining muted for longer than 4 years.
Furthermore, management has hinted that they wish to acquire new oil assets at presently depressed prices to increase daily production rate to a targeted 20,000 bbl by end-2021. They have room to acquire debt, but they may also wish to issue equity for this purpose. Hence, the possibility of dilution to existing shareholders cannot be entirely ruled out.
However, given management’s historical track record of prioritizing ROI and optimal capital allocation, and in consideration of the fact that the MD owns 10% of outstanding shares, there is some assurance that any potential acquisitions will be accretive to EPS and therefore valuations.

Opportunities

As with the existence of risk, the presence of material opportunities also looms over the company. Some of them are discussed below:
· Increased Daily Oil Production Rate
· Inclusion of 2C Resources
· Future oil prices exceeding $50 and effects from coronavirus dissipating
Increased Daily Oil Production Rate
The first and most obvious opportunity is the potential for increased production rate. We’ve seen in the last quarter (2Q21) that the North Sabah field increased its daily production rate by approximately 20% as a result of production enhancement projects (infill drilling), lowering OPEX/bbl as a result. To vastly oversimplify, infill drilling is the process of maximizing well density by drilling in the spaces between existing wells to improve oil production.
The same improvements are being undertaken at the Anasuria field via infill drilling, subsea debottlenecking, water injection and sidetracking of existing wells. Without boring you with industry jargon, this basically means future production rate is likely to improve going forward.
By how much can the oil production rate be improved by? Management estimates in their analyst presentation that enhancements in the Anasuria field will be able to yield 5,000 bbl/day by 2021 (current: 2,500 bbl/day).
Similarly, improvements in the North Sabah field is expected to yield 7,000 bbl/day by 2021 (current: 5,300 bbl/day).
This implies a total 2021 expected daily production rate from the two fields alone of 12,000 bbl/day (current: 8,000 bbl/day). That’s a 50% increase in yields which we haven’t factored into our valuation yet.
Furthermore, we haven’t considered any production from existing 2C resources (e.g. Marigold/Sunflower) or any potential acquisitions which may occur in the future. By management estimates, this can potentially increase production by another 8,000 bbl/day, bringing total production to 20,000 bbl/day.
While this seems like a stretch of the imagination, it pays to keep them in mind when forecasting future revenues and valuations.
Just to play around with the numbers, I’ve come up with a sensitivity analysis of possible annual EBITDA at different oil prices and daily oil production rates:
Table 2: Hibiscus EBITDA at different oil price and daily oil production rates
https://preview.redd.it/jnpfhr5n9br41.png?width=814&format=png&auto=webp&s=bbe4b512bc17f576d87529651140cc74cde3d159
The left column represents different oil prices while the top row represents different daily oil production rates.
The green column represents EBITDA at current daily production rate of 8,000 bbl/day; the orange column represents EBITDA at targeted daily production rate of 12,000 bbl/day; while the purple column represents EBITDA at maximum daily production rate of 20,000 bbl/day.
Even conservatively assuming increased estimated annual ITDA of RM 500m (FY20: RM 318m), and long-term average oil prices of $50 (FY20: $60), the estimated Net Profit and P/E ratio is potentially lucrative at daily oil production rates of 12,000 bbl/day and above.
2C Resources
Since we’re on the topic of improved daily oil production rate, it bears to pay in mind the relatively enormous potential from Hibiscus’s 2C Resources. North Sabah’s 2C Resources alone exceed 30 mmbbl; while those from the yet undiagnosed Marigold/Sunflower fields also reach 30 mmbbl. Altogether, 2C Resources exceed 70 mmbbl, which dwarfs the 44 mmbbl of 2P Reserves we have considered up to this point in our valuation estimates.
To refresh your memory, 2C Resources represents oil volumes which have been discovered but are not yet classified as “commercial”. This means that there is reasonable certainty of the oil being recoverable, as opposed to simply being in the very early stages of exploration. So, to be conservative, we will imagine that only 50% of 2C Resources are eligible for reclassification to 2P reserves, i.e. 35 mmbbl of oil.
https://preview.redd.it/mto11iz7abr41.png?width=375&format=png&auto=webp&s=e9028ab0816b3d3e25067447f2c70acd3ebfc41a
This additional 35 mmbbl of oil represents an 80% increase to existing 2P reserves. Assuming the daily oil production rate increases similarly by 80%, we will arrive at 14,400 bbl/day of oil production. According to Table 2 above, this would yield an EBITDA of roughly RM 630m assuming $50 oil.
Comparing that estimated EBITDA to FY20’s actual EBITDA:
FY20 FY21 (incl. 2C) Difference
Daily oil production (bbl/day) 8,626 14,400 +66%
Average oil price (USD/bbl) $68.57 $50 -27%
Average OPEX/bbl (USD) $16.64 $20 +20%
EBITDA (RM ‘m) 632 630 -
Hence, even conservatively assuming lower oil prices and higher OPEX/bbl (which should decrease in the presence of higher oil volumes) than last year, we get approximately the same EBITDA as FY20.
For the sake of completeness, let’s assume that Hibiscus issues twice the no. of existing shares over the next 10 years, effectively diluting shareholders by 50%. Even without accounting for the possibility of the acquisition of new oil fields, at the current market capitalization of RM 714m, the prospective P/E would be about 10x. Not too shabby.
Future oil prices exceeding $50 and effects from coronavirus dissipating
Hibiscus shares have recently been hit by a one-two punch from oil prices cratering from $60 to $30, as a result of both the Saudi-Russian dispute and depressed demand for oil due to coronavirus. This has massively increased supply and at the same time hugely depressed demand for oil (due to the globally coordinated lockdowns being implemented).
Given a long enough timeframe, I fully expect OPEC+ to come to an agreement and the economic effects from the coronavirus to dissipate, allowing oil prices to rebound. As we equity investors are aware, oil prices are cyclical and are bound to recover over the next 10 years.
When it does, valuations of O&G stocks (including Hibiscus’s) are likely to improve as investors overshoot expectations and begin to forecast higher oil prices into perpetuity, as they always tend to do in good times. When that time arrives, Hibiscus’s valuations are likely to become overoptimistic as all O&G stocks tend to do during oil upcycles, resulting in valuations far exceeding reasonable estimates of future earnings. If you can hold the shares up until then, it’s likely you will make much more on your investment than what we’ve been estimating.

Conclusion

Wrapping up what we’ve discussed so far, we can conclude that Hibiscus’s market capitalization of RM 714m far undershoots reasonable estimates of fair value even under conservative assumptions of recoverable oil volumes and long-term average oil prices. As a value investor, I hesitate to assign a target share price, but it’s safe to say that this stock is worth at least RM 1.00 (current: RM 0.45). Risk is relatively contained and the upside far exceeds the downside. While I have no opinion on the short-term trajectory of oil prices, I can safely recommend this stock as a long-term Buy based on fundamental research.
submitted by investorinvestor to SecurityAnalysis [link] [comments]

Suggest any free forex back tester or forex simulator.

I am new to Forex. I am reading a lot. Practicing on demo account. I want to back test my strategy but i can't find anything good.
I have come across few simulators online but they are trail variants and their scale is limited as well.
They download small amount of history data. For longer variant they are asking for payment. They are whoopingpy expensive . I have yet to make anything on Forex. Let alone pay them 200-300 dollars for their full subscription .
Or please suggest a free way to do it. Is it possible on meta trader to practice on history data?
I think problem with meta trader is that you see the future easily. It doesn't hide it for the sake of practice.
submitted by nasir9998 to Forex [link] [comments]

How to optimise the speed of my Pandas code?

Hi learnpython,
My first attempt at writing my own project. Prior to this I had never used classes or Pandas so it's been a difficult learning curve. I was hoping to get some feedback on the overall structure - does everything look sensible? Are there better ways of writing some bits?
I also wanted to specifically check how I can increase the execution speed. I currently iterate rows which Pandas did say will be slow, but I couldn't see a workaround. The fact it is quite slow makes me think there is a better solution that I'm missing.
To run the code yourself download a .csv of Forex data and store in same folder as script - I used Yahoo finance GBP USD.
"""This program simulates a Double SMA (single moving average) trading strategy. The user provides a .csv file containing trade history and two different window sizes for simple moving averages (smallest number first). The .csv must contain date and close columns - trialled on Yahoo FX data). The program will generate a 'buy' signal when the short SMA is greater than the long SMA, and vice versa. The results of each trade are stored and can be output to a .csv file.""" import pandas as pd class DoubleSMA(): """Generates a Double SMA trading system.""" def __init__(self, name, sma_a, sma_b): """Don't know what goes here.""" self.name = name self.sma_a = sma_a self.sma_b = sma_b self.index = 0 self.order = 'Start' self.signal = '' def gen_sma(self, dataset, sma): """Calculates SMA and adds as column to dataset.""" col_title = 'sma' + str(sma) dataset[col_title] = dataset['Close'].rolling(sma).mean() return dataset def gen_signal(self, row, dataset): """Generates trade signal based on comparison of SMAs.""" if row[0] == (dataset.shape[0] - 1): #Reached final line of dataset; close current trade. self.order = 'Finish' elif row[3] > row[4]: self.signal = 'Buy' elif row[3] < row[4]: self.signal = 'Sell' def append_result(row, result, order): """Adds 'entry' details to results dataframe (i.e. opens trade).""" result = result.append({"Entry date": row[1], "Pair": "GBPUSD", "Order": order, "Entry price": row[2]}, ignore_index=True) return result def trade(row, order, signal, index, result): """Executes a buy or sell routine depending on signal. Flips between 'buy' and 'sell' on each trade.""" if order == 'Start': order = signal result = append_result(row, result, order) elif order == 'Finish': result.iloc[index, 1] = row[1] result.iloc[index, 5] = row[2] elif order != signal: #Close current trade result.iloc[index, 1] = row[1] result.iloc[index, 5] = row[2] index += 1 order = signal result = append_result(row, result, order) return order, index, result def result_df(): """Creates a dataframe to store the results of each trade.""" result = pd.DataFrame({"Entry date": [], "Exit date": [], "Pair": [], "Order": [], "Entry price": [], "Exit price": [], "P/L": []}) return result def dataset_df(): """Opens and cleans up the data to be analysed.""" dataset = pd.read_csv('GBPUSD 2003-2020 Yahoo.csv', usecols=['Date', 'Close']) dataset.dropna(inplace=True) dataset['Close'] = dataset['Close'].round(4) return dataset def store_result(result): """Outputs results table to .csv.""" result.to_csv('example.csv') def calc_pl(result): """Calculates the profil/loss of each row of result dataframe.""" pass #Complete later dataset = dataset_df() result = result_df() sma_2_3 = DoubleSMA('sma_2_3', 2, 3) dataset = sma_2_3.gen_sma(dataset, sma_2_3.sma_a) dataset = sma_2_3.gen_sma(dataset, sma_2_3.sma_b) dataset.dropna(inplace=True) dataset.reset_index(inplace=True, drop=True) for row in dataset.itertuples(): sma_2_3.gen_signal(row, dataset) sma_2_3.order, sma_2_3. index, result = trade(row, sma_2_3.order, sma_2_3.signal, sma_2_3.index, result) calc_pl(result) print(result) store_result(result) 
submitted by tbYuQfzB to learnpython [link] [comments]

Super Scalp 2.0 mt4 Indicator

Super Scalp 2.0 mt4 Indicator
Download free mt4 Indicators: https://www.forexwinners.in/
Super Scalp 2.0 mt4 Indicator is a combination of Metatrader 4 (MT4) indicator(s) and template. Super Scalping technique is an excellent technique that uses 5 minutes timeframe using AUDUSD, EURUSD, GBPUSD pairs. Indicators used are:
Super Scalp 2.0 mt4 Indicator is a combination of Metatrader 4 (MT4) indicator(s) and template. provides an opportunity to detect various peculiarities and patterns in price dynamics that are invisible to the naked eye. The essence of this forex strategy is to transform the accumulated history data and trading signals.

https://preview.redd.it/3h3htoer4hd41.png?width=997&format=png&auto=webp&s=f2ca4b3eb95df4f2cb4cc0cf9bfa186b66a8ccf8
submitted by mt4indicators to u/mt4indicators [link] [comments]

What are the risks and targets?

First of all I'm new in the Forex (and I'm reading "babypips" :-).
I'm trying to look into day-trading, but before opening even demo account I decided to experiment with some historical data.
I found and downloaded 2 years of USD/CAD history of 1-minute candles. Then I wrote a simple script that opens a long position every minute (using open ASK price) and looks when it reaches either the target or stop-loss.
The parameters of each trade:
For calculating position parameters I'm using the following formulas:
With given parameters the used leverage is about 1:30.

My testing on historical data shows that in average there're just 2 or 3 entry points in a day. I'm confused with my next steps, because I think it's not enough to create and test a working strategy.

Should I decrease the target? Or increase leverage and risks? What are the usual targets and risks in the day trading?
Thank you!
submitted by DrunkBystander to Forex [link] [comments]

[educational] Technical analysis, patterns, and charts analysis for the day trader

[educational] Technical analysis, patterns, and charts analysis for the day trader
Chart patterns form a key part of day trading. Candlestick and other charts produce frequent signals that cut through price action “noise”.
The best patterns will be those that can form the backbone of a profitable day trading strategy, whether trading stocks, cryptocurrency of forex pairs.
Every day you have to choose between hundreds of trading opportunities. This is a result of a wide range of factors influencing the market. Day trading patterns enable you to decipher the multitude of options and motivations – from hope of gain and fear of loss, to short-covering, stop-loss triggers, hedging, tax consequences and plenty more.
Candlestick patterns help by painting a clear picture, and flagging up trading signals and signs of future price movements. Whilst it’s said you’ll need to use technical analysis to succeed day trading with candlestick and other patterns, it’s important to note utilizing them to your advantage is more of an art form than a rigid science.
You have to learn the power of chart patterns and the theory that governs them in order to identify the best patterns to supplement your trading style and strategies.

Use In Day Trading

Used correctly trading patterns can add a powerful tool to your arsenal. This is because history has a habit of repeating itself and the financial markets are no exception. This repetition can help you identify opportunities and anticipate potential pitfalls.
RSI, volume, plus support and resistance levels all aide your technical analysis when you’re trading. But crypto chart patterns play a crucial role in identifying breakouts and trend reversals. Mastering the art of reading these patterns will help you make smarter trades and bolster your profits, as highlighted in the highly regarded, ‘stock patterns for day trading’, by Barry Rudd.

Breakouts & Reversals

In the patterns and charts below you’ll see two recurring themes, breakouts and reversals.
  • Breakout – A breakout is simply when the price clears a specified critical level on your chart. This level could by any number of things, from a Fibonacci level, to support, resistance or trend lines.
  • Reversal – A reversal is simply a change in direction of a price trend. That change could be either positive or negative against the prevailing trend. You may also hear it called a ‘rally’, ‘correction’, or ‘trend reversal’.

Candlestick Charts

Candlestick charts are a technical tool at your disposal. They consolidate data within given time frames into single bars. Not only are the patterns relatively straightforward to interpret, but trading with candle patterns can help you attain that competitive edge over the rest of the market.
They first originated in the 18th century where they were used by Japanese rice traders. Since Steve Nison introduced them to the West with his 1991 book ‘Japanese Candlestick Charting Techniques’, their popularity has surged.
Below is a break down of three of the most popular candlestick patterns used for day trading.

Shooting Star Candlestick

This is often one of the first you see when you open a chart with candlestick patterns. This bearish reversal candlestick suggests a peak. It is precisely the opposite of a hammer candle. It won’t form until at least three subsequent green candles have materialized. This will indicate an increase in price and demand. Usually, buyers lose their cool and clamber for the price to increasing highs before they realize they’ve overpaid.
The upper shadow is usually twice the size of the body. This tells you the last frantic buyers have entered trading just as those that have turned a profit have off-loaded their positions. Short-sellers then usually force the price down to the close of the candle either near or below the open. This traps the late arrivals who pushed the price high. Panic often kicks in at this point as those late arrivals swiftly exit their positions.

https://preview.redd.it/gf5dwjhbrdh31.png?width=300&format=png&auto=webp&s=437ff856bfd6ebc95da34528462ba224d964f01f

Doji Candlestick

One of the most popular candlestick patterns for trading forex is the doji candlestick (doji signifies indecision). This reversal pattern is either bearish or bullish depending on the previous candles. It will have nearly, or the same open and closing price with long shadows. It may look like a cross, but it can have an extremely small body. You will often get an indicator as to which way the reversal will head from the previous candles.
If you see previous candles are bullish, you can anticipate the next one near the underneath of the body low will trigger a short/sell signal when the doji lows break. You’ll then see trail stops above the doji highs.
Alternatively, if the previous candles are bearish then the doji will probably form a bullish reversal. Above the candlestick high, long triggers usually form with a trail stop directly under the doji low.
These candlestick patterns could be used for intraday trading with forex, stocks, cryptocurrencies and any number of other assets. But using candlestick patterns for trading interpretations requires experience, so practice on a demo account before you put real money on the line.

https://preview.redd.it/4yo650lcrdh31.png?width=300&format=png&auto=webp&s=b2aa3cdeef23e44e1e3e3047bbe2604fce0a4768

Hammer Candlestick

This is a bullish reversal candlestick. You can use this candlestick to establish capitulation bottoms. These are then normally followed by a price bump, allowing you to enter a long position.
The hammer candlestick forms at the end of a downtrend and suggests a near-term price bottom. The lower shadow is made by a new low in the downtrend pattern that then closes back near the open. The tail (lower shadow), must be a minimum of twice the size of the actual body.
The tails are those that stopped out as shorts started to cover their positions and those looking for a bargain decided to feast. Volume can also help hammer home the candle. To be certain it is a hammer candle, check where the next candle closes. It must close above the hammer candle low.
Trading with Japanese candlestick patterns has become increasingly popular in recent decades, as a result of the easy to glean and detailed information they provide. This makes them ideal for charts for beginners to get familiar with.

https://preview.redd.it/7snzz8qdrdh31.png?width=300&format=png&auto=webp&s=f83ff82f0980dd30c33bc6886ae7e7ed3a98b72f

More Popular Day Trading Patterns

Using Price Action

Many strategies using simple price action patterns are mistakenly thought to be too basic to yield significant profits. Yet price action strategies are often straightforward to employ and effective, making them ideal for both beginners and experienced traders.
Put simply, price action is how the price is likely to respond at certain levels of resistance or support. Using price action patterns from pdfs and charts will help you identify both swings and trendlines.
Whether you’re day trading stocks or forex or crypto with price patterns, these easy to follow strategies can be applied across the board.

Zone Strategy

So, how do you start day trading with short-term price patterns? you will likely employ a ‘zone strategy’. One obvious bonus to this system is it creates straightforward charts, free from complex indicators and distractions.

https://preview.redd.it/7e5x37zerdh31.png?width=300&format=png&auto=webp&s=2098a4c9df4a4556c3024cec1c176ce50c9806c0

Dead Zone

This empty zone tells you that the price action isn’t headed anywhere. There is no clear up or down trend, the market is at a standoff. If you want big profits, avoid the dead zone completely. No indicator will help you makes thousands of pips here.

The Red Zone

This is where things start to get a little interesting. Once you’re in the red zone the end goal is in sight, and that one hundred pip winner within reach. For example, if the price hits the red zone and continues to the upside, you might want to make a buy trade. It could be giving you higher highs and an indication that it will become an uptrend.
This will be likely when the sellers take hold. If the price hits the red zone and continues to the downside, a sell trade may be on the cards. You’d have new lower lows and a suggestion that it will become a downtrend.

The End Zone

This is where the magic happens. With this strategy, you want to consistently get from the red zone to the end zone. Draw rectangles on your charts like the ones found in the example. Then only trade the zones. If you draw the red zones anywhere from 10-20 pips wide, you’ll have room for the price action to do its usual retracement before heading to the downside or upside.

Outside Bar At Resistance Or Support

You’ll see a bullish outside bar if today’s low exceeded yesterdays, but the stock still rallies and closes above yesterday’s high. If the complete opposite price action took place, you’d have yourself the perfect bearish example.
Unfortunately, it isn’t as straightforward as identifying an outside candlestick and then just placing a trade. It’s prudent to find an outside day after a major break of a trend.

https://preview.redd.it/egb0lp6grdh31.png?width=300&format=png&auto=webp&s=b0170eceea5006464e5832bc3a9083c72ee677ad

Spring At Support

The spring is when the stock tests the low of a range, but then swiftly comes back into trading zone and sets off a new trend. One common mistake traders make is waiting for the last swing low to be reached. However, as you’ve probably realized already, trading setups don’t usually meet your precise requirements so don’t stress about a few pennies.

https://preview.redd.it/q82lap2hrdh31.png?width=300&format=png&auto=webp&s=9e40f0bc25c2df06a1d93edb68b293c858a32592

Little To No Price Retracement

Put simply, less retracement is proof the primary trend is robust and probably going to continue. Forget about coughing up on the numerous Fibonacci retracement levels. The main thing to remember is that you want the retracement to be less than 38.2%. This means even when today’s asset tests the previous swing, you’ll have a greater chance that the breakout will either hold or continue towards the direction of the primary trend.

https://preview.redd.it/ey997b2irdh31.png?width=300&format=png&auto=webp&s=c938aac51e3b3bbf1f45a11c46f4ae3dfd1b6dd4
Trading with price patterns to hand enables you to try any of these strategies. Find the one that fits in with your individual trading style. Remember, you’ll often find the best trading chart patterns aren’t overly complex, instead they paint a clear picture using minimal indicators, reducing the likelihood of mistakes and distraction.

Consider Time Frames

When you start trading with your short term price patterns pdf to hand, it’s essential you also consider time frames in your calculations. In your market, you’ll find a number of time frames simultaneously co-existing. This means you can find conflicting trends within the particular asset your trading. Your stock could be in a primary downtrend whilst also being in an intermediate short-term uptrend.
Many traders make the mistake of focusing on a specific time frame and ignoring the underlying influential primary trend. Usually, the longer the time frame the more reliable the signals. When you reduce your time frames you’ll be distracted by false moves and noise.
Many traders download examples of short-term price patterns but overlook the underlying primary trend, do not make this mistake. You should trade-off 15-minute charts, but utilize 60-minute charts to define the primary trend and 5-minute charts to establish the short-term trend.

Wrapping Up

Our understanding of chart patterns has come along way since the initial 1932 work of Richard Schabacker in ‘Technical Analysis and Stock Market Profits’. Schabacker asserted then, ‘any general stock chart is a combination of countless different patterns and its accurate analysis depends upon constant study, long experience and knowledge of all the fine points, both technical and fundamental…’ So whilst there is an abundance of patterns out there, remember accurate analysis and sustained practice is required to fully reap their benefits.

The source : https://www.daytrading.com/patterns
submitted by JalelTounsi to ethfinance [link] [comments]

MEDICALCHAIN REVIEW

A lot of people are talking about this project and as a potential investor i decided to run some checks on it and see if it's worth. Everyone is pumped about it but after a closer look I'm not sure it's worth its price. Therefore let's have a look at the facts :
Medicalchain.com LTD
The company was incorporated on 28/06/2017 under the name MEDICALCHAIN.IO LTD as a private company limited by ordinary shares.
Three directors are appointed: Mr. Mohammed Tayeb, Mr. Abdullah Dafir Albeyatti and Mr. Bara Mustafa.
The initial shareholdings (total of 999) are split in 3 equal parts:
1/3 (333) owned by Mr. Abdullah Dafir Albeyatti
1/3 (333) owned by Mr. Bara Mustafa
1/3 (333) owned by XL CAPITAL VENTURES LTD (owned by Mohammed Tayeb and Omar Tayeb)
At 24/07/2017 the number of shares is increased to 1332, with XL CAPITAL VENTURES LTD holding 666 ordinary shares.
At 13/08/2017 XL CAPITAL VENTURES LTD cease to be a shareholder, with MR Mohammed Tayeb now holding the 666 shares previously held by XL Capital.
At 14/08/2018 MEDICALCHAIN.IO LTD becomes MEDICALCHAIN.COM LTD
Below you can find a bit about every member of the team starting with the top dogs.
MOHAMMED TAYEB :: Director
MR MOHAMMED TAYEB description taken from medicalchain.com.
Mr. Mohammed Tayeb is a Partner at Hearn Capital Limited. Mr. Tayeb co-founded ReadyCache. In 2010, he headed up the development side of morethan.com. During his time there, Mr. Tayeb architected and developed a system to drive down online fraud, saving its over £40 million. Prior to that, he ran a boutique consultancy business in the field of mobile web and application development. Together with his brother and Co-Founder, they own over eight games and utility applications on the Apple and Google Play apps market, with over 10 million downloads. Mr. Tayeb is an internet entrepreneur, investor, and founder of several technology and e-commerce start-ups. As well being a Partner in Hearn Capital, he is also a Non-Executive Director on the board of Salic. Mr. Tayeb specializes in bringing together technological efficiencies to the business world. He has a degree in e-commerce from Brunel University and an Executive MBA from the University of Oxford.
I’ve done an extensive background check and noticed that Mr Mohammed Tayeb has had his fingers in many pies since 2010 being appointed director in and out of more than 15 companies. I am not sure if I would trust him with my money as it looks like he cant commit to something for a longer period of time.
Below you can find part of his work history:
MONSTER TECHNOLOGIES LTD :: Director since 9 January 2017 :: Active - no information found
HEARN CAPITAL LIMITED :: Director since 21 January 2016 :: Active
The company is owned from background by Influential (Holdings) Limited owning more than 50% of shares with a total equity of £1.7 mil.
Basically Influential Holding has lent Hearn Capital 1.2 million to invest in different companies. To me it looks like Mohammed has no skin in this as Influential Holdings Limited is owned by Mr Andrew Richardson and Mr John Edward Simpson.
GOODSHAW CAPITAL MANAGEMENT LTD :: Director since 6 January 2016 :: Company still active Dormant company aka not carrying any business activity
DYNISTICS LIMITED :: Director since 03/03/2015 :: Company still active
Acquisition of Dynistics https://www.dynistics.com/ :: a software company that Hearncapital bought in 2015 which counts colleges, NHS Foundation Trusts and recruitment agencies as clients.
Link for acquisition:https://www.insidermedia.com/insidemidlands/141513-hearn-capital-buys-solihull-software-company
Dynistics is listed as a “small company” and the directors have elected not to include a copy of the profit and loss account within the financial statements. Total equity registered at the end of 2016 : £16.557
SALIC(UK)Limited :: Director since 22/01/2015 :: Company still active
Saudi Agricultural and Livestock investment company: this company belongs to Ministry Of Finance (Saudi Arabia) and financed with over 300 million pounds in capital and 75% or more ownership.
The Travel booking Company LTD :: Director since 19/11/2014 : Dissolved 29/03/2016
XL Capital Investment LTD :: Director 17/03/2014 :: Dissolved : 04/07/2017 - no other information found
Global Labs Technology Limited :: Director since 10/12/2013 –Dissolved : 18/07/2016 - No record, probably westernlabs.com which has no track record nor an online presence
Ready Cache Technologies LTD :: Director since 01/10/2013 :: Dissolved : 04/07/2017 - ReadyCache is a website that accelerates your online content and delivers the best possible speeds to you.
Pepperstone Limited :: Director 13/02/2017 – 22/06/2017 :: Resigned (former 123FX.COM LTD)
Pepperstone acquired 123FX.COM LTD what is now its UK subsidiary from Mohammed Tayeb, who alongside his broker Omar Tayeb established an FCA regulated shell with plans to launch a retail forex brokerage called 123FX.com. The business was never launched, and instead was sold to Pepperstone in late 2015.
Pepperstone has suspended trading for clients in its UK subsidiary as the company is making some changes to its management structure and processes, and bringing on some additional resources in its UK office. To comply with its FCA obligations Pepperstone has had to temporarily suspend trading in the UK until all of the changes are complete and the additional resources are in place.
http://www.checkdirector.co.uk/directomohammed-tayeb/ https://www.leaprate.com/forex/brokers/pepperstone-swaps-phil-horner-mohammed-tayeb-board-uk-fx-relaunch/
Some other companies he had been involved with:
• Director House of Choice Stores LTD :: 2013 – 2016 Disolved
• Director XI Capital Ventures LTD :: 2014-2017 Disolved
• Director UR Trading :: 2002 – 2010 Disolved
• Director LOVEFRAGRANCE INTERNATIONAL LTD :: 2012 – Dissolved 2013
• Director DAR FIRST LIMITED :: 2007 – Dissolved 2011
• Director BLACKSTONE E-COMMERCE LIMITED :: 2011 – Dissolved 2013
All this information can be found at https://beta.companieshouse.gov.uk/ . You just lookup his name or company names and access the records.
** Mr. Abdullah Dafir Albeyatti :: Director**
Enthusiastic doctor with a wide range of skills and interests. Currently completing my general practice training in Leeds. Previous surgical trainee in the London Deanery. My ambition is to continue improving as a doctor and to develop myself in other fields of medicine and aesthetic training.
He is also the founder of dischargesummary.co.uk. The website is described as a website designed to streamline and lessen the work load placed on junior doctors by standardising the content of discharge summaries produced when a patient is discharged from hospital. This platform has allowed hospital departments to establish quality assurance and accurately produce reliable discharge summaries to effectively commute between hospital and community medicine.
I checked the website and 3 out of 6 features are under development. The site is now redirected to https://ds.medicalchain.com/
Mr. Bara Mustafa :: DirectoCTO
There is no mention of Mr Bara Mustafa on the mediclchain.com which is weird as he is one of the directors and shareholder. It looks like Mr Bara occupation is CTO, which surely would be of interested to the public. Mr Bara is also a OwneDirector of ENETIDEAS LTD since 2010 rendering services as IT Consultant.
https://www.enetideas.com :: the website is not functional, none of the products links are working.
Jay Povey :: Blockchain developer at medicalchain
His introductory linkedin line :
Self taught programmer, programming for 7+ years. BA(Hons) from Buckinghamshire New University. Since January I joined Medicalchain to help create a world class blockchain platform for electronic health records. Previously worked on forex trading algorithm using deep learning / pattern recognition techniques. I have had a keen interest in blockchain technology over the past 2 years. I have been learning the ins and outs of the technology and Im very excited about the future of blockchain. I see great potential for revolutionizing the way businesses are run.
He started coding for Medicalchain in 2017 previously working for 2 years for one of Mohammed companies ReadyCache which was dissolved.
I’m not sure about his experience developing “on forex trading algorithm” as at the previous company ReadyCache they were “building software that is making a difference to webmasters and large companies. We accelerate our customers’ websites, save our customers money and enhance user experience”
Before ReadyCache he worked for a college as an IT technicial and e-learning advisor.
Also I’m not sure what to make out of his facebook profile, he comes across a bit weird. Also on one of his facebook posts he was asking where you can buy bitcoin in may 2017 but on his summary : “I have had a keen interest in blockchain technology over the past 2 years. I have been learning the ins and outs of the technology and Im very excited about the future of blockchain”
https://www.facebook.com/jaypov
Robert Miller :: Director Of Business Development
Looks like his CV is somehow better than the rest, again worked alongside Mohammed at Goodshaw Capital for 1 year. Freelanced a few blockchain projects so I would say he is the one who will drive the project as longs as the money are coming in.
Linkedin https://www.linkedin.com/in/bertcmiller
Natalie Furness :: Communication Director
She has a background in healthcare industry, namely working as a physiotherapist since 2010. She also lectured for Physiotherapy and Sports Exercise Scientist students at Birmingham University and currently working as a project manager for a company offering solutions to the occupational health sector.
The rest of the team occupies either associates or consultants positions for a short period of time 2-3 months.
To sum it up : the initial £5000 pre-ICO investment is way way exaggerated based on the fact that there is nothing to show for at the present moment, just ideas. Mr Mohammed has started 15+ companies and most of them are dissolved which doesn't sound very promising. On top of that he now owns half the company whereas initially the company was split in thirds. Their CTO is not mentioned anywhere on the website but he is a shareholder in the company.
I would not recommend investing in the project right now and I would wait to see if the project would ever gain traction and materialize.
Below I listed some of the videos related to Medicalchain at different conferences / interviews :
https://www.youtube.com/watch?v=W4Bc4RiugMg
https://www.youtube.com/watch?v=F6WbFMt6Ic4
https://www.youtube.com/watch?v=NT-vRXZ2k-o
https://www.youtube.com/watch?v=devzmfzsh7E
https://www.youtube.com/watch?v=SA91OAaNZUo
https://www.youtube.com/watch?v=h_OdMREOpBI
https://www.youtube.com/watch?v=ebP5ZzQView
If you want me to run any other checks on other upcoming ICO please let me know.
submitted by cryptoflorin to ICOAnalysis [link] [comments]

MEDICALCHAIN REVIEW

A lot of people are talking about this project and as a potential investor i decided to run some checks on it and see if it's worth. Everyone is pumped about it but after a closer look I'm not sure it's worth its price. Therefore let's have a look at the facts :
Medicalchain.com LTD
The company was incorporated on 28/06/2017 under the name MEDICALCHAIN.IO LTD as a private company limited by ordinary shares.
Three directors are appointed: Mr. Mohammed Tayeb, Mr. Abdullah Dafir Albeyatti and Mr. Bara Mustafa.
The initial shareholdings (total of 999) are split in 3 equal parts:
1/3 (333) owned by Mr. Abdullah Dafir Albeyatti
1/3 (333) owned by Mr. Bara Mustafa
1/3 (333) owned by XL CAPITAL VENTURES LTD (owned by Mohammed Tayeb and Omar Tayeb)
At 24/07/2017 the number of shares is increased to 1332, with XL CAPITAL VENTURES LTD holding 666 ordinary shares.
At 13/08/2017 XL CAPITAL VENTURES LTD cease to be a shareholder, with MR Mohammed Tayeb now holding the 666 shares previously held by XL Capital.
At 14/08/2018 MEDICALCHAIN.IO LTD becomes MEDICALCHAIN.COM LTD
Below you can find a bit about every member of the team starting with the top dogs.
MOHAMMED TAYEB :: Director
MR MOHAMMED TAYEB description taken from medicalchain.com.
Mr. Mohammed Tayeb is a Partner at Hearn Capital Limited. Mr. Tayeb co-founded ReadyCache. In 2010, he headed up the development side of morethan.com. During his time there, Mr. Tayeb architected and developed a system to drive down online fraud, saving its over £40 million. Prior to that, he ran a boutique consultancy business in the field of mobile web and application development. Together with his brother and Co-Founder, they own over eight games and utility applications on the Apple and Google Play apps market, with over 10 million downloads. Mr. Tayeb is an internet entrepreneur, investor, and founder of several technology and e-commerce start-ups. As well being a Partner in Hearn Capital, he is also a Non-Executive Director on the board of Salic. Mr. Tayeb specializes in bringing together technological efficiencies to the business world. He has a degree in e-commerce from Brunel University and an Executive MBA from the University of Oxford.
I’ve done an extensive background check and noticed that Mr Mohammed Tayeb has had his fingers in many pies since 2010 being appointed director in and out of more than 15 companies. I am not sure if I would trust him with my money as it looks like he cant commit to something for a longer period of time.
Below you can find part of his work history:
MONSTER TECHNOLOGIES LTD :: Director since 9 January 2017 :: Active - no information found
HEARN CAPITAL LIMITED :: Director since 21 January 2016 :: Active
The company is owned from background by Influential (Holdings) Limited owning more than 50% of shares with a total equity of £1.7 mil.
Basically Influential Holding has lent Hearn Capital 1.2 million to invest in different companies. To me it looks like Mohammed has no skin in this as Influential Holdings Limited is owned by Mr Andrew Richardson and Mr John Edward Simpson.
GOODSHAW CAPITAL MANAGEMENT LTD :: Director since 6 January 2016 :: Company still active Dormant company aka not carrying any business activity
DYNISTICS LIMITED :: Director since 03/03/2015 :: Company still active
Acquisition of Dynistics https://www.dynistics.com/ :: a software company that Hearncapital bought in 2015 which counts colleges, NHS Foundation Trusts and recruitment agencies as clients.
Link for acquisition:https://www.insidermedia.com/insidemidlands/141513-hearn-capital-buys-solihull-software-company
Dynistics is listed as a “small company” and the directors have elected not to include a copy of the profit and loss account within the financial statements. Total equity registered at the end of 2016 : £16.557
SALIC(UK)Limited :: Director since 22/01/2015 :: Company still active
Saudi Agricultural and Livestock investment company: this company belongs to Ministry Of Finance (Saudi Arabia) and financed with over 300 million pounds in capital and 75% or more ownership.
The Travel booking Company LTD :: Director since 19/11/2014 : Dissolved 29/03/2016
XL Capital Investment LTD :: Director 17/03/2014 :: Dissolved : 04/07/2017 - no other information found
Global Labs Technology Limited :: Director since 10/12/2013 –Dissolved : 18/07/2016 - No record, probably westernlabs.com which has no track record nor an online presence
Ready Cache Technologies LTD :: Director since 01/10/2013 :: Dissolved : 04/07/2017 - ReadyCache is a website that accelerates your online content and delivers the best possible speeds to you.
Pepperstone Limited :: Director 13/02/2017 – 22/06/2017 :: Resigned (former 123FX.COM LTD)
Pepperstone acquired 123FX.COM LTD what is now its UK subsidiary from Mohammed Tayeb, who alongside his broker Omar Tayeb established an FCA regulated shell with plans to launch a retail forex brokerage called 123FX.com. The business was never launched, and instead was sold to Pepperstone in late 2015.
Pepperstone has suspended trading for clients in its UK subsidiary as the company is making some changes to its management structure and processes, and bringing on some additional resources in its UK office. To comply with its FCA obligations Pepperstone has had to temporarily suspend trading in the UK until all of the changes are complete and the additional resources are in place.
http://www.checkdirector.co.uk/directomohammed-tayeb/ https://www.leaprate.com/forex/brokers/pepperstone-swaps-phil-horner-mohammed-tayeb-board-uk-fx-relaunch/
Some other companies he had been involved with:
• Director House of Choice Stores LTD :: 2013 – 2016 Disolved
• Director XI Capital Ventures LTD :: 2014-2017 Disolved
• Director UR Trading :: 2002 – 2010 Disolved
• Director LOVEFRAGRANCE INTERNATIONAL LTD :: 2012 – Dissolved 2013
• Director DAR FIRST LIMITED :: 2007 – Dissolved 2011
• Director BLACKSTONE E-COMMERCE LIMITED :: 2011 – Dissolved 2013
All this information can be found at https://beta.companieshouse.gov.uk/ . You just lookup his name or company names and access the records.
** Mr. Abdullah Dafir Albeyatti :: Director**
Enthusiastic doctor with a wide range of skills and interests. Currently completing my general practice training in Leeds. Previous surgical trainee in the London Deanery. My ambition is to continue improving as a doctor and to develop myself in other fields of medicine and aesthetic training.
He is also the founder of dischargesummary.co.uk. The website is described as a website designed to streamline and lessen the work load placed on junior doctors by standardising the content of discharge summaries produced when a patient is discharged from hospital. This platform has allowed hospital departments to establish quality assurance and accurately produce reliable discharge summaries to effectively commute between hospital and community medicine.
I checked the website and 3 out of 6 features are under development. The site is now redirected to https://ds.medicalchain.com/
Mr. Bara Mustafa :: DirectoCTO
There is no mention of Mr Bara Mustafa on the mediclchain.com which is weird as he is one of the directors and shareholder. It looks like Mr Bara occupation is CTO, which surely would be of interested to the public. Mr Bara is also a OwneDirector of ENETIDEAS LTD since 2010 rendering services as IT Consultant.
https://www.enetideas.com :: the website is not functional, none of the products links are working.
Jay Povey :: Blockchain developer at medicalchain
His introductory linkedin line :
Self taught programmer, programming for 7+ years. BA(Hons) from Buckinghamshire New University. Since January I joined Medicalchain to help create a world class blockchain platform for electronic health records. Previously worked on forex trading algorithm using deep learning / pattern recognition techniques. I have had a keen interest in blockchain technology over the past 2 years. I have been learning the ins and outs of the technology and Im very excited about the future of blockchain. I see great potential for revolutionizing the way businesses are run.
He started coding for Medicalchain in 2017 previously working for 2 years for one of Mohammed companies ReadyCache which was dissolved.
I’m not sure about his experience developing “on forex trading algorithm” as at the previous company ReadyCache they were “building software that is making a difference to webmasters and large companies. We accelerate our customers’ websites, save our customers money and enhance user experience”
Before ReadyCache he worked for a college as an IT technicial and e-learning advisor.
Also I’m not sure what to make out of his facebook profile, he comes across a bit weird. Also on one of his facebook posts he was asking where you can buy bitcoin in may 2017 but on his summary : “I have had a keen interest in blockchain technology over the past 2 years. I have been learning the ins and outs of the technology and Im very excited about the future of blockchain”
https://www.facebook.com/jaypov
Robert Miller :: Director Of Business Development
Looks like his CV is somehow better than the rest, again worked alongside Mohammed at Goodshaw Capital for 1 year. Freelanced a few blockchain projects so I would say he is the one who will drive the project as longs as the money are coming in.
Linkedin https://www.linkedin.com/in/bertcmiller
Natalie Furness :: Communication Director
She has a background in healthcare industry, namely working as a physiotherapist since 2010. She also lectured for Physiotherapy and Sports Exercise Scientist students at Birmingham University and currently working as a project manager for a company offering solutions to the occupational health sector.
The rest of the team occupies either associates or consultants positions for a short period of time 2-3 months.
To sum it up : the initial £5000 pre-ICO investment is way way exaggerated based on the fact that there is nothing to show for at the present moment, just ideas. Mr Mohammed has started 15+ companies and most of them are dissolved which doesn't sound very promising. On top of that he now owns half the company whereas initially the company was split in thirds. Their CTO is not mentioned anywhere on the website but he is a shareholder in the company.
I would not recommend investing in the project right now and I would wait to see if the project would ever gain traction and materialize.
Below I listed some of the videos related to Medicalchain at different conferences / interviews :
https://www.youtube.com/watch?v=W4Bc4RiugMg
https://www.youtube.com/watch?v=F6WbFMt6Ic4
https://www.youtube.com/watch?v=NT-vRXZ2k-o
https://www.youtube.com/watch?v=devzmfzsh7E
https://www.youtube.com/watch?v=SA91OAaNZUo
https://www.youtube.com/watch?v=h_OdMREOpBI
https://www.youtube.com/watch?v=ebP5ZzQView
If you want me to run any other checks on other upcoming ICO please let me know.
submitted by cryptoflorin to Crypto_ICO_Investing [link] [comments]

machine learning problem

Hi guys, I have a problem. We are trying machine learning on forex. We are trying to predict direction of "tommorows" candle. Due using machine learning with python we have decided using Daily charts, because daily is more accurate and it's not taking too much time.
The reason why Im typing here is asking for help, because Im not sure how to handle this problem and where to take data from.
Todays mechanical process is -. Open Metatrader -> History center -> export data (for each individual currency pair) -> save as .csv and then parse data and work with this. [Its exhausting] This is how we obtain data for machine learning. Plain for using was like: Everyday when I come home from work (about 7 PM), I download data, let my model to predict "future", then I make some market orders and go sleep. But there is a problem. When I come home I can have data only for yesterday's candle, information about todays candle are missing, because day has not ended yet. I cant manualy obrtain data for today but it won't be todays EOD data, but it will be today's "till 7 PM data). So in this case, my model can not work properly, because it has been learned on EOD prices.
I'm desperate. I don't know how to solve this problem. I'm trying to figure this out about 2 weeks. I'm still nowhere. Can anyone suggest me something? Does anyone has any idea please?
Thank you so much and have a wonderful day.
submitted by ferryboy to algotrading [link] [comments]

Download historical Forex data for FREE in 3 Simple Steps How to download historical price data from Yahoo to Excel ... Interactive Brokers Historical Data Downloader - YouTube Part 6 - Importing Historical Data From MetaTrader ... Download FOREX candlestick tick data for FREE using Python ... How To Download Historic Data Using The MetaTrader 4 ... Download Historical Exchange Rates into Excel with a Click ...

Download Free Forex Data. Download Step 1: Please, select the Application/Platform and TimeFrame! In this section you'll be able to select for which platform you'll need the data. MetaTrader 4 / MetaTrader 5. This platform allows the usage of M1 (1 Minute Bar) Data only. These files are well suited for backtesting trading strategies under MetaTrader 4 and MetaTrader 5 platform. Please, select ... Forex Tester allows you to import an unlimited number of currency pairs and years of history data in almost any possible text format (ASCII *.csv, *.txt). We strongly recommend importing 1-minute data for accurate testing (it is possible to import higher timeframes but testing results may not be as good). This Excel spreadsheet downloads historical Forex data from the Internet. You can ask for bid, ask and mid rates for a range of historical currencies. You can use this data to backtest your trading strategies, and perform technical analysis (such as plotting the EMA, RSI or MACD). The spreadsheet is easy to use. You simply enter two three-letter currency symbols, two dates, and specify whether ... GVI Forex Database: Free Forex Historical Data. The GVI database program provides daily free forex historical data (close high low) to the euro start (January 1, 1999). The most up to date forex data for major forex (currency) pairs and crosses downloadable to your spreadsheet. For instructions on how to access the free forex data, click here. Download End of Day FOREX Stock Data, Intraday Data and Historical Quotes. ... Before you can download our data, you will first need to register. Registration is FREE and will allow you to access our end of day data and symbol history. To register now click HERE. 2 Subscribe. We offer a range of Membership options ranging from free services to comprehensive end of day data updates. Full ... Step 1: Download the history data file you would like to add to the MT4 platform and save to your computer. Step 2: From MT4: "Tools — History Center" or by pressing F2: Step 3: Select forex and select the correct symbol according to your requirements. Choose 1 Minute: Step 4: Select Import, click browse and choose your file location, select the data file and click ok once the data is ... Download and copy the history forex files: Load the necessary data in Forex Strategy Builder (CSV) format. 100 000 bars is a good start. Copy and paste the downloaded forex data files in the new Data Source directory. Now the new data will be available in the Editor.

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Download historical Forex data for FREE in 3 Simple Steps

http://www.amazon.com/Honest-Guide-Stock-Trading-Market-Beating-ebook/dp/B00IRR20V0/ In this video you will learn how to transfer historical price data from ... This video is for those traders that find their charts do not display enough bars. In this video I will show you how to download fresh historical data for yo... This is my first video where I show my face and talk, so please do not be so brutal on me in comments for saying "actually" or "fx good quality data" too man... Simple software that allows you download historical data from IB TWS (Interactive Brokers Trader Workstation) Supports historical data for: stocks and future... This is the 6th part of the series for building and trading Expert Advisors with Forex Strategy Builder Professional and MetaTrader. In this episode we will ... How to Backtest and download forex history data to you computer - Duration: 30:02. Fit Money 62,454 views. 30:02. Get FREE historical data for Amibroker in 3 Simple Steps - Duration: 5:05. ... Download the Excel file that can pull historical exchange rate data right into Excel with a click. http://www.excelclout.com/historical-exchange-rates-in-exc...

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